1. Last 7 days
    1. https://www.facebook.com/groups/4770669677posts/posts/10164413651119678/

      The Haas Atlas (page 7) would identify the foundry mark as being a Hulse-Typen manufactured Pica face for Smith-Corona: https://archive.org/.../typewriter.../page/n5/mode/2up

      Page 31 of the 1954 Smith-Corona Typewriter Type Styles and Keyboards Catalog would indicate it seems to match up solidly with the Pica Gothic No. 4 typeface: https://typecast.munk.org/.../1954-smith-corona-scm.../ Especially look at the Q, the J, and the 8. The '64 NOMDA guide Mark links is the same, but doesn't have as extensive a typesample.

      Many machines, and especially the American manufactured ones, don't always have foundry marks which line up with the respective catalogs.

    Annotators

    1. The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.

      the performance of the predictions reduce with on long forecastin periods

    1. Precision is defined as the ratio of accurately predicted positive cases to the total predicted positives [146], while Recall assesses the ratio of correctly predicted positives to all actual positive instances [15]. The F-measure harmonizes Precision and Recall, emphasizing a balanced consideration of false positives and false negatives

      this is how we can mesure if the ia model is working properly and giving resaults

    2. examined stock market prediction using ML algorithms, focusing on technical and fundamental analyses. Linear regression showed low error in predicting closing prices, while SVM achieved 76% accuracy for sentiment analysis. Despite these results, the models lack precision for long-term investments or reliable decision-making. Hybrid approaches combining both analyses show potential for improved accuracy, offering a promising area for future research. In 202

      Supports Kaushik: accuracy drops with longer horizons.

    3. While regression and time series models are foundational and easy to implement, their predictive power is often limited by assumptions of linearity and stationarity. Regression models perform well in stable market conditions but may fail to capture complex, nonlinear relationships without hybridization. Time series models like ARIMA are effective for short-term forecasting but are sensitive to parameter tuning and may not adapt well to sudden market changes or outlier events. Integrating these traditional methods with feature selection or preprocessing techniques can improve robustness, but their effectiveness is still largely dependent on data quality and the specific financial context.

      limitations are not only for ai, traditional models fail in complex environments too

    4. Sentiment analysis, by incorporating unstructured data from various sources, provides a unique perspective on market behavior. Although challenges like data noise, misinformation, and variability in sentiment intensity persist, integrating sentiment analysis with machine learning and deep learning techniques has proven to significantly enhance stock market predictions.

      the noicy data and other challenges can hide the real important information leading the ai model to take bad desitions, so the main problem is the false information, the key is to have reliable and true information

    5. Several promising avenues for future research exist:Contextual integration of external events such as political changes and global occurrences into prediction models can enhance their robustness.Developing models that adapt to real-time market conditions while addressing noise and unanticipated events remains a critical goal.Increasing the interpretability of prediction models could provide deeper insights into market behavior and build trust among investors.Leveraging metaheuristic algorithms to optimize NN weights and architectures is another potential research area.Expanding the scope of analysis to derivatives-based markets and hybrid approaches could yield significant advancements.

      solutions to the problems listed above

    1. all disagreements

      I suppose this formula was in the PAP, but still… is there a good reason why we calculate this as a share of disagreements rather than as a share of all interactions?; any chance this could give us significance when evaluating the effect of more /less heterogenous cases?

    1. When dealing with simple arguments, the first two columns of a truth table are guide columns in which the variables, or letters, of the argument are listed, followed by a column for each premise and then a column for the conclusion. In this case, though, the guide columns happen to be identical to the premise columns, so we won’t repeat them.

      How a truth table works

    2. (Note: The rules themselves are set out in lowercase letters; capital letters, though, are used to symbolize statements in a proof.)

      What to look out for

    3. Notice that the rules are not used jointly but are applied one at a time and once per line. They can, however, be applied multiple times in the same proof, either sequentially or occasionally.

      What to keep in mind

    1. What are the chances that the supercharger station even has power after a hurricane? You want something that is already on the property. I've had roads blocked by fallen trees for days after a windstorm event.If you are concerned about riding through a 14 day+ event you need a generator.

      This person has just read and is responding to a true account of someone describing something involving their neighbor that actually happened. Why does the person responding approach it like a hypothetical and include claims like "you need a generator"? (As a point of fact, they didn't need a generator.)

      Reading this comment reminds me that this sort of thing used to be a lot more common on HN—I'd run into this bizarre phenomenon at least once every couple weeks or so—but I'd totally forgotten about it.

    1. There are actor networks, but it's the actors with agency who can choose to do things differently.

      This statement explains the importance of human actors in a system as complex and seemingly abstract as the economy. It is in my opinion, what informs lobbying. Lobbyists aim to influence policy on behalf of individuals, corporations, and various interest groups by appealing to the individuals who are in charge of policy-making.

      When it comes to TMftF, this is what Mary Murphy is up to. She is out and about trying to lobby for the Ministry to get important stakeholders to adopt the Carbon Coin and CarbonQuantitative Easing.(CQE)

      On this website of the National Conference of State Legislature, lobbying is generally defined as "seeking to influence government action through written or oral communication." Different states have different criteria for lobbying.

    1. The land is burning. Percy stands on high, 2235  And either we or they must lower lie.⌜He exits.⌝ FALSTAFF  2236  Rare words, brave world!—Hostess, my breakfast, 2237  come.— 2238  O, I could wish this tavern were my drum.

      The scene opposes the gravity of civil war to Falstaff’s cynicism. Hal speaks in solemn rhyming verse about the burning land, and Falstaff answers in prose asking for his breakfast. His last wish, that the tavern could be his drum, sums him up: war without leaving comfort.

    2. Thou knowest in the 2194 175 state of innocency Adam fell, and what should poor 2195  Jack Falstaff do in the days of villainy? Thou seest I 2196  have more flesh than another man and therefore 2197  more frailty. You confess, then, you picked my 2198  pocket.

      Falstaff blames human nature and his own weight. He compares himself to Adam: if the first man fell in paradise, how could poor Jack resist in a corrupt world? He even turns his fat into a theological argument, more flesh meaning more frailty. Nothing is ever his fault.

    3. Go to, you are a woman, go. HOSTESS  2085 Who, I? No, I defy thee! God’s light, I was 2086  never called so in mine own house before. FALSTAFF  2087 Go to, I know you well enough. HOSTESS  2088 No, Sir John, you do not know me, Sir John. I 2089 70 know you, Sir John. You owe me money, Sir John, 2090  and now you pick a quarrel to beguile me of it. I 2091  bought you a dozen of shirts to your back. FALSTAFF  2092 Dowlas, filthy dowlas. I have given them 2093  away to bakers’ wives; they have made bolters of 2094 75 them. HOSTESS  2095 Now, as I am a true woman, holland of eight 2096  shillings an ell. You owe money here besides, Sir 2097  John, for your diet and by-drinkings and money 2098  lent you, four-and-twenty pound.

      Falstaff reverses the roles to intimidate her and escape his debts. He attacks her because she’s a woman and easier to bully, but she sees through it and says so. It’s the same trick as after the robbery, except this time his victim is a woman who works for her money.

    4. Well, I’ll repent, and that suddenly, while I am in 2025  some liking. I shall be out of heart shortly, and then 2026  I shall have no strength to repent.

      Irony: Falstaff talks about repenting while finding reasons not to do it. His excuse is brilliant. He must repent while he’s still fat, because once thin he won’t have the strength. He promised the same thing in Act I, and he still blames bad company instead of himself.

    1. Percy is but my factor, good my lord, 1987  To engross up glorious deeds on my behalf. 1988  And I will call him to so strict account 1989 155 That he shall render every glory up,

      Hal treats Hotspur as a simple agent collecting glory on his behalf, and plans to claim it all back. He turns honour into a business account, while Hotspur treats it as a sacred quest. Two opposite visions — and the accountant is the one who wins.

    2. Why, Harry, do I tell thee of my foes, 1962  Which art my nearest and dearest enemy?

      A paradox: his closest and dearest person is also his enemy. Just before, the King accuses Hal of being ready to fight for Hotspur against his own father. He puts his son on the rebels’ side, which is the worst accusation in the play.

    3. By being seldom seen, I could not stir 1883  But like a comet I was wondered at, 1884 50 That men would tell their children “This is he.” 1885  Others would say “Where? Which is Bolingbroke?” 1886  And then I stole all courtesy from heaven, 1887  And dressed myself in such humility 1888  That I did pluck allegiance from men’s hearts, 1889 55 Loud shouts and salutations from their mouths, 1890  Even in the presence of the crownèd king. 1891  Thus did I keep my person fresh and new,

      The King reveals the strategy that won him the crown: appearing rarely so people admired him like a comet, and stealing their loyalty with calculated humility. The irony is huge — it’s exactly Hal’s own plan from his first soliloquy. The father lectures his son about something the son already does.

    4. out of my blood p. 131 1842  He’ll breed revengement and a scourge for me. 1843  But thou dost in thy passages of life 1844 10 Make me believe that thou art only marked 1845  For the hot vengeance and the rod of heaven 1846  To punish my mistreadings

      The King sees his rebellious son as a punishment sent by God to pay for the way he took the throne. He reads Hal’s behaviour as divine revenge for Richard II’s murder. His guilt poisons even his relationship with his own child.

    1. I understand thy kisses, and thou mine, 1770  And that’s a feeling disputation; 1771  But I will never be a truant, love, 1772  Till I have learned thy language; for thy tongue 1773 215 Makes Welsh as sweet as ditties highly penned, 1774  Sung by a fair queen in a summer’s bower, 1775  With ravishing division, to her lute. GLENDOWER  1776  Nay, if you melt, then will she run mad. The Lady speaks again in Welsh. MORTIMER  1777  O, I am ignorance itself in this! GLENDOWER  1778 220 She bids you on the wanton rushes lay you down 1779  And rest your gentle head upon her lap, 1780  And she will sing the song that pleaseth you, 1781  And on your eyelids crown the god of sleep, 1782  Charming your blood with pleasing heaviness, 1783 225 Making such difference ’twixt wake and sleep 1784  As is the difference betwixt day and night p. 127 1785  The hour before the heavenly harnessed team 1786  Begins his golden progress in the east. MORTIMER  1787  With all my heart I’ll sit and hear her sing. 1788 230 By that time will our book, I think, be drawn. GLENDOWER  1789  Do so, and those musicians that shall play to you 1790  Hang in the air a thousand leagues from hence, 1791  And straight they shall be here. Sit and attend. HOTSPUR  1792  Come, Kate, thou art perfect in lying down. 1793 235 Come, quick, quick, that I may lay my head in thy 1794  lap.

      Shakespeare opposes the tenderness of Mortimer and his wife to the roughness of Hotspur and Kate. Mortimer and his Welsh wife don’t share a language and still understand each other, while Hotspur and Kate both speak English and don’t. Love has nothing to do with words.

    2. you are too willful-blame, 1741  And, since your coming hither, have done enough 1742  To put him quite besides his patience. 1743 185 You must needs learn, lord, to amend this fault. 1744  Though sometimes it show greatness, courage, 1745  blood— 1746  And that’s the dearest grace it renders you— 1747  Yet oftentimes it doth present harsh rage, 1748 190 Defect of manners, want of government, 1749  Pride, haughtiness, opinion, and disdain,

      Hotspur is unbearable even for his own allies: his uncle lists his arrogance and bad manners. But Worcester admits this fire can also look like greatness — his quality and his flaw are the same thing. Hotspur’s answer, “I am schooled”, shows he isn’t listening at all.

    3. See how this river comes me cranking in 1661  And cuts me from the best of all my land 1662  A huge half-moon, a monstrous ⌜cantle⌝ out. 1663 105 I’ll have the current in this place dammed up,

      It shows Hotspur’s arrogance: he already acts like a tyrant over land he hasn’t conquered. They argue about the course of a river while the kingdom still belongs to the King, and he wants to redirect the Trent for a few acres. The rebellion is divided before the first battle.

    4. I can call spirits from the vasty deep. HOTSPUR  1614  Why, so can I, or so can any man, 1615  But will they come when you do call for them?

      Hotspur destroys Glendower’s credibility by showing his magic is only words: anyone can call spirits, the question is whether they answer. The line applies to the whole rebellion, where everyone promises armies that never arrive

    5. At my nativity 1572  The front of heaven was full of fiery shapes, 1573 15 Of burning cressets, and at my birth 1574  The frame and huge foundation of the Earth 1575  Shaked like a coward. HOTSPUR  1576   var _____WB$wombat$assign$function_____ = function(name) {return (self._wb_wombat && self._wb_wombat.local_init && self._wb_wombat.local_init(name)) || self[name]; }; if (!self.__WB_pmw) { self.__WB_pmw = function(obj) { this.__WB_source = obj; return this; } } { let window = _____WB$wombat$assign$function_____("window"); let self = _____WB$wombat$assign$function_____("self"); let document = _____WB$wombat$assign$function_____("document"); let location = _____WB$wombat$assign$function_____("location"); let top = _____WB$wombat$assign$function_____("top"); let parent = _____WB$wombat$assign$function_____("parent"); let frames = _____WB$wombat$assign$function_____("frames"); let opener = _____WB$wombat$assign$function_____("opener"); let arguments; {window.addEventListener('load', alignSplitLines.bind(null,'sftln-1576','ftln-1575','E')); }}Why, so it would have done p. 113 1577  At the same season if your mother’s cat 1578 20 Had but kittened

      The scene opposes Glendower’s theatrical mysticism to Hotspur’s cynicism: one claims the heavens announced his birth, the other answers that the same would happen if a cat had kittens. The problem is political — Hotspur humiliates an ally he needs, minutes before dividing the kingdom.

    1. Tulajdonos jelleg == (belföldi magánszemély vagy külföldi magánszemély) (shareholderType) Számlastátusz vizsgálata Heritage guarding számlastátusz esetén nem kerülhet bele a TMF-be a sor. (heritageGuarding) “Heritage guarding account status“

      Ez alá tegyünk be :<br /> A megfeleltetett állomány esetében az első részvényszerzés dátumát feltölteni a készletadatokból.

      A dátum formátuma: ÉÉÉÉHHNN

      Adattöltés KÖTELEZŐ: ● ha az érintett helyszámla típusa = MEGBÍZÓI › ha a társasági esemény megrendelője = Kibocsátó és › ha a Kibocsátó a Tpt. 149.§ (3) bekezdése alá tartozik = Igen. Valamennyi más esetben az adattöltés mindkét mező esetében opcionális. De törzsadatszinten nincs definiálva adott kibocsátónál, hogy a 149 paragrafus alá tartozik e ezért a megvalósítás minden megfeleltetés esetén ugyan az. A készletadatokból dolgozik, és veszi az első bekerülés dátumát. .

    2. KID validáció

      Ez alá szerintem tegyünk be :

      Jogszabály Tpt. 149.§ (3) bekezdésének való megfelelés A Tpt. 149.§ (3) szerint:

      A tulajdonosi megfeleltetés során, amennyiben az olyan kibocsátó kérelmére indul, amely befektetési vállalkozás, részvénytársasági formában működő árutőzsdei szolgáltató, befektetési alapkezelő, tőzsde, központi szerződő fél, illetve központi értéktár, az értékpapírszámla-vezető a kibocsátó részvényeit jegyző értékpapírszámla tulajdonosairól a központi értéktárnak köteles átadni a (2) bekezdésben meghatározottakon kívül a részvényszerzés részvénykönyvbe történő bejegyzésének, továbbá jogszabály eltérő rendelkezése esetén a részvény szerzésének időpontját.

      Az MNB-vel történt egyeztetésnek megfelelően a részvények részvényszerzés időpontjára vonatkozóan azon első részvényszerzés dátum megadása szükséges, amely során a részvényesnek állománya keletkezett.

      A KID-re, a társasági eseményhez import fájl-ban a 586-593 pozícióban várja a KELER ezen adatot. A fejlesztés keretében, amennyiben a kibocsátó a Tpt. 149. § (3) bekezdése alá tartozik, ezen dátum adat megadása kötelezően elvárt.

    3. Validációk

      Validációj alá kellene egy KELER kód lista:

      02-TMF befjegyre 10 - osztalék fizetés névre;

      18 -részvénykönyv aktualizálás;<br /> 20 – tulajdonosi megfeleltetés kötvény 27 - osztalék előleg névre 30 – közgyűlés zárolás nélkül; 50- Tulajdonosi megfeleltetés részvényre 51 - Rendkívüli közgyűlés<br /> 90 - visszamenőleges tulajdonosi megfeleltetés

    4. Ez akkor lehet, ha már ki lett generálva a TMF egy adott rekordról, mely minden TMF validáción átment, de nem érkezett vissza hozzá kapcsolódóan semmilyen információ még

      Exportált státusz értéket, akkor veheti fel

    5. em használt 586-600 Szóközzel feltöltve N

      EZ módosításra kerül: Részvényszerzés RKV bejegyzés dátuma 586-593 Dátum, ÉÉÉÉHHNN formában I/N

    6. Empty, ha még egyáltalán nem vol TMF generálás a hozamjog sorhoz

      Itt csak egy "T2 hiányzott a " volt" szóból:-)) Empty, ha még egyáltalán nem volt TMF generálás a hozamjog sorhoz

    7. Ez akkor lehet, ha már ki lett generálva a TMF egy adott rekordról, viszont nem ment át egy vagy több validáción. Ezeket a rekordokat a rendszer nem állítja elő és nem generálja bele a fájlba.

      Helyette: Nem exportálhaó státuszt akkor veheti fel a TMF, ha már elindult a TMF generálás egy adott rekordról, viszont nem ment át egy vagy több validáción. Ezeket a rekordokat a rendszer nem állítja elő és nem generálja bele a fájlba.

    8. Ez akkor lehet, ha már ki lett generálva a TMF egy adott rekordról, mely minden TMF validáción átment, de nem érkezett vissza hozzá kapcsolódóan semmilyen információ még
      Helyette : Ezt az exportált státuszt akkor veszi fel a TMF, ha már le lett generálva a TMF egy adott rekordról, mely minden TMF validáción átment, de nem érkezett vissza hozzá kapcsolódóan további info.
      
    9. Ez akkor lehet, ha még egyáltalán nem volt TMF generálás az adott hozamjog sorhoz. Alapértelmezett státusz.

      Függő státusz abban az esetben lehetséges, ha a TMF generálás még nem történt meg. Alapértelmezett státusz.

    1. I’ll procure this fat rogue a 1554  charge of foot, and I know his death will be a march 1555  of twelve score. The money shall be paid back again 1556  with advantage. Be with me betimes in the morning, 1557  and so good morrow, Peto.

      Hal is always one move ahead. In the same speech he protects Falstaff from the sheriff, pays back the stolen money, and jokes that the march will kill him. He saves him and condemns him in the same breath, which shows how cold he really is.

    2. banish not him thy 1485  Harry’s company. Banish plump Jack, and banish 1486  all the world. PRINCE  1487 I do, I will.

      The game turns dark: Hal announces the break with this world and with Falstaff. “I do, I will” is the key — “I do” still belongs to the game, “I will” is a real promise. Falstaff hears it, and the knocking at the door interrupts everything.

    3. there is virtue in that 1432  Falstaff; him keep with, the rest banish. And tell me 1433 445 now, thou naughty varlet, tell me where hast thou 1434  been this month?

      Falstaff uses the King’s role to praise himself and ask Hal to keep him. He describes himself as a noble, cheerful gentleman and makes the “King” banish everyone except him. Behind the joke there’s a real request: he needs Hal to choose him.

    4. was it for 1268 280 me to kill the heir apparent? Should I turn upon the 1269  true prince? Why, thou knowest I am as valiant as 1270  Hercules, but beware instinct. The lion will not 1271  touch the true prince. Instinct is a great matter. 1272  I was now a coward on instinct

      Falstaff turns the whole situation around: he claims his cowardice was actually loyalty, because his instinct recognised the true prince. He uses an old belief about lions sparing royal blood to escape. It works

    5. Four 1193 205 rogues in buckram let drive at me. PRINCE  1194 What, four? Thou said’st but two even now. FALSTAFF  1195 Four, Hal, I told thee four. POINS  1196 Ay, ay, he said four. FALSTAFF  1197 These four came all afront, and mainly 1198 210 thrust at me. I made me no more ado, but took all 1199  their seven points in my target, thus. PRINCE  1200 Seven? Why there were but four even now. FALSTAFF  1201 In buckram? POINS  1202 Ay, four in buckram suits. FALSTAFF  1203 215Seven by these hilts, or I am a villain else. PRINCE, ⌜to Poins⌝  1204 Prithee, let him alone. We shall have 1205  more anon. FALSTAFF  1206 Dost thou hear me, Hal? PRINCE  1207 Ay, and mark thee too, Jack. p. 87 FALSTAFF  1208 220Do so, for it is worth the listening to. These 1209  nine in buckram that I told thee of

      The joke is that the lie grows with every line: the enemies multiply so Falstaff can look like a hero. Hal and Poins know the truth and let him sink, counting out loud. Nobody believes him for a second — what’s funny is his nerve, not his story.

    1. The Kaiser Family Foundation (KFF) recently reported that around 25% of immigrants are treated differently or unfairly by their health care provider.

      KFF who is an organization that focuses on health policy research and health journalism, have found millions are prone to not getting the same health care service as proficient english speakers.

    1. Memoria comunicativa (cotidiana, de horizonte temporal limitado) frente a memoria cultural (objetivada, duradera)J. Assmann (1995), Collective memory and cultural identity, New German Critique, 65, 125-133JSTOR (acceso desde la UPV). Resume su teoría, que desarrolló en Das kulturelle Gedächtnis (1992; inglés, 2011)La memoria cultural involucra a varias generaciones y se apoya en medios, instituciones y ritosA. Assmann, Cultural memory and Western civilization (Cambridge UP, 2011)

      esto ta esta

    2. La nostalgia también existe a nivel de grupo y fortalece la pertenenciaWildschut, Bruder, Robertson, van Tilburg y Sedikides (2014), Journal of Personality and Social Psychology, 107(5), 844-863

      5.3

    1. Most previous studies on VIs have focused on their humanlike appearance and its impact, and the results are inconsistent

      slight research gap, as the studies are inconsistent

    1. Игра 2 4 6. эта тройка соответсвтует правилу. твоя цель понять это правила.

      для ты можешь говорить новые тройки, а я буду в ответ говорить соответствует ли твоя тройка правилу

    1. eLife Assessment

      This fundamental study identifies GVIN as a previously unrecognized interferon-induced antimicrobial GTPase and establishes its role in cell-autonomous immunity against intracellular Burkholderia. The compelling data demonstrate that GVIN assembles into coat-like structures on the bacterial surface and restricts BimA-dependent actin-based motility through a mechanism independent of GBP1. These findings expand our understanding of interferon-induced host defense mechanisms and reveal a distinct pathway by which human cells restrict the intracellular spread of bacterial pathogens.

    2. Reviewer #1 (Public review):

      This is a very elegant and experimentally convincing study. Using systematic screening of actin tail formation in two bacterial strains and employing a panel of CRISPR-CAS ko cell lines, the authors identify the dynamin-related GTPases GVIN as antibacterial agent, which forms an oligomeric coat around an intracellular Burkholderia strain. The bacterial O-antigen LPS layer is required for the formation of the GVIN coat, which disturbs the polar localization of the bacterial actin-polymerizing BimA protein.

      The experiments appear of high quality, figures are well prepared, and clean and statistically significant results are provided. I have no criticism on the presented approaches.

      The identification of a novel GBP1-independent pathway targeting intracellular bacteria is not only of fundamental importance for the immunity field, but also of high interest to researchers in other areas, for example, evolutionary or structural biologists.

      Comments on revised version.

      This is a beautiful study.

    3. Reviewer #2 (Public review):

      Public summary

      Summary:

      The authors wanted to investigate how cells defend against intracellular pathogens, such as Shigella and Burkholderia species, that coopt the host actin machinery to spread from cell-to-cell. Previous work has identified IFNg-inducible GTPase of the Guanylate Binding Protein (GBP) family in cytosolic defence against Gram-negative bacteria. By forming a coat around Shigella, human GBP1 suppresses actin-based motility by displacing IcsA, which is the actin-polymerising virulence factor present at bacterial poles. In addition, GBP1 recruits the cytosolic LPS-sensor, caspase-4, to the bacterial surface, which results in the removal of bacterial replicative niches via pyroptotic cell death. Here they followed up their finding that GBP1 can reduce actin-based motility of Shigella in HeLa cells and surprisingly fails to do so during Burkholderia infection. In contrast, in T24 bladder epithelial cells, GBP1 is competent in blocking Burkholderia actin-tails. They therefore wanted to identify the GBP1-independent factor that blocks actin-based motility in IFNg-treated cells that is absent in HeLa cells.

      Major strengths and weaknesses of the methods and results:

      The authors report a second IFNg-dependent pathway involving the protein product of the gene GVIN1, which was previously thought to be a pseudogene. GVIN1 (GTPase, very large interferon inducible 1) is thus the first human member of this family of ~250 kDa putative GTPases to be demonstrated to be functional and have potential antimicrobial roles. The discovery that GVIN1 is indeed functional, forms coats on Burkholderia in an LPS O-antigen-dependent manner, and limits actin-dependent motility are the main strengths of this paper. The authors use CRISPR/Cas9-based knockouts in HeLa and T24 cells, and complement them, to demonstrate GBP1 and GVIN1 are both required to inhibit actin-based motility.

      An appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

      The authors achieved their main goals through well-planned experiments and unbiased screens. They succeeded in finding the factor that blocks actin-based motility independently of GBP1. This is driven by GVIN1, which coats bacteria and limiting actin-tail formation by reducing the expression of BimA through currently unknown mechanisms. Further, they found that an O-antigen mutant can escape coating by GVIN1, indicating the requirement for these polysaccharides in GVIN1-dependent bacterial sensing. However, the authors have not investigated whether GVIN1, which has two GTPase-domains, does indeed have GTPase activity and whether GVIN1 and GBP1 together completely block cell-to-cell spread by Burkholderia and thereby restrict bacterial numbers over the infection time course. They also do not show whether GBP1 and GVIN1 target the same bacterial cell or different populations of bacteria.

      A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

      This work uncovers the antimicrobial actions of a member of yet another family of IFNg-induced GTPases, which potentially acts against other intracellular pathogens. GVIN1 appears to operate independently and in parallel to GBP1, pointing to the breadth and complexity of the IFNg-inducible GTPase families.

      Comments on revised version.

      They have addressed my comments.

    4. Reviewer #3 (Public review):

      Summary:

      Here, Guo et al. (2025) propose that the IFN-induced GTPase GVIN1 forms a coat on cytosolic Burkholderia thailandensis, blocking actin tail formation through a mechanism analogous to GBP1-mediated restriction of Shigella motility.

      Their study was prompted by the intriguing observation that IFNγ priming and GBP1 coat formation fail to inhibit B. thailandensis actin-based motility in HeLa cells, yet IFNγ restricts the motility of Burkholderia in T24 cells. Further investigation revealed that IFNγ restricts B. thailandensis motility in T24 cells via both GBP1-dependent and -independent mechanisms, suggesting that HeLa cells lack a critical GBP1 co-factor required to inhibit actin tail formation.

      To identify the GBP1-independent mechanism, the authors performed an siRNA screen of interferon-stimulated genes (ISGs) and identified GVIN1, a large IFN-induced GTPase, as essential for restricting B. thailandensis motility. To identify the GBP1-independent mechanism, perform a knock-down screen for ISGs and find that the loss of GVIN, a very large IFN-induced GTPase, results in higher actin tail positive B. thailandensis in T24 cells. They further demonstrate that GVIN forms coats on the surface of B. thailandensis which prevent the polar localization of BimA and thus actin tail formation. In summary, the data reveal two independent IFNγ-induced pathways that restrict bacterial motility: one GBP1-dependent and the other GVIN1-dependent, each relying on distinct host co-factors.

      Global assessment:

      This is a well-executed study that convincingly demonstrates how GVIN1 restricts actin-based motility of B. thailandensis through the assembly of coatomers. The results are clearly described, the manuscript is easy to follow, and the data are overall compelling and well presented. I have only a few suggestions on how the manuscript could be further improved.

      Comments on revised version.

      The authors have answered all my points.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      This is a very elegant and convincing study. Using systematic screening of actin tail formation in two bacterial strains and employing a panel of CRISPR-CAS ko cell lines, the authors identify a novel dynamin-related GTPase GVIN, which forms an oligomeric coat around an intracellular Burkholderia strain. The bacterial O-antigen LPS layer is required for the formation of the GVIN coat, which disturbs the polar localization of the bacterial actin-polymerizing BimA protein.

      I am not an expert in infection studies, but the experiments appear to be of high quality, the figures are well prepared, and clean and statistically significant results are provided. I have no criticism of the presented approaches.

      The identification of a novel GBP1-independent pathway targeting intracellular bacteria is not only of fundamental importance for the immunity field but also of high interest to researchers in other areas, for example, evolutionary or structural biologists.

      We sincerely thank the reviewer for their generous appraisal of our work. We appreciate their encouraging feedback.

      Reviewer #2 (Public review):

      Summary:

      The authors wanted to investigate how cells defend against intracellular pathogens, such as Shigella and Burkholderia species, that co-opt the host actin machinery to spread from cell-to-cell. Previous work has identified IFNg-inducible GTPase of the Guanylate Binding Protein (GBP) family in cytosolic defence against Gram-negative bacteria. By forming a coat around Shigella, human GBP1 suppresses actin-based motility by displacing IcsA, which is the actin-polymerising virulence factor present at bacterial poles. In addition, GBP1 recruits the cytosolic LPS-sensor, caspase-4, to the bacterial surface, which results in the removal of bacterial replicative niches via pyroptotic cell death. Here, they followed up their finding that GBP1 can reduce actin-based motility of Shigella in HeLa cells and, surprisingly, fails to do so during Burkholderia infection. In contrast, in T24 bladder epithelial cells, GBP1 is competent in blocking Burkholderia actin-tails. They therefore wanted to identify the GBP1-independent factor that blocks actin-based motility in IFNγ-treated cells that is absent in HeLa cells.

      Major strengths and weaknesses of the methods and results:

      The authors report a second IFNγ-dependent pathway involving the protein product of the gene GVIN1, which was previously thought to be a pseudogene. GVIN1 (GTPase, very large interferon inducible 1) is thus the first human member of this family of ~250 kDa putative GTPases to be demonstrated to be functional and have potential antimicrobial roles. The discovery that GVIN1 is indeed functional, forms coats on Burkholderia in an LPS O-antigen-dependent manner, and limits actin-dependent motility are the main strengths of this paper. The authors use CRISPR/Cas9-based knockouts in HeLa and T24 cells, and complement them to demonstrate that GBP1 and GVIN1 are both required to inhibit actin-based motility.

      An appraisal of whether the authors achieved their aims and whether the results support their conclusions:

      The authors achieved their main goals through well-planned experiments and unbiased screens. They succeeded in finding the factor that blocks actin-based motility independently of GBP1. This is driven by GVIN1, which coats bacteria and limits actin-tail formation by reducing the expression of BimA through currently unknown mechanisms. Further, they found that an O-antigen mutant can escape coating by GVIN1, indicating the requirement for these polysaccharides in GVIN1-dependent bacterial sensing. However, the authors have not investigated whether GVIN1, which has two GTPase domains, does indeed have GTPase activity and whether GVIN1 and GBP1 together completely block cell-to-cell spread by Burkholderia and thereby restrict bacterial numbers over the infection time course. They also do not show whether GBP1 and GVIN1 target the same bacterial cell or different populations of bacteria.

      A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community:

      This work uncovers the antimicrobial actions of a member of yet another family of IFNg-induced GTPases, which potentially acts against other intracellular pathogens. GVIN1 appears to operate independently and in parallel to GBP1, pointing to the breadth and complexity of the IFNγ-inducible GTPase families.

      We thank the reviewer for their overall positive assessment of our work and their suggestions for additional studies. We addressed all the comments and in response we have added additional experimental data to the revised manuscript. However, while we agree that a detailed biochemical characterization of GVIN1 protein is warranted, we consider such experiments outside of the scope of the current work.

      Reviewer #3 (Public review):

      Summary:

      Here, Guo et al. (2025) propose that the IFN-induced GTPase GVIN1 forms a coat on cytosolic Burkholderia thailandensis, blocking actin tail formation through a mechanism analogous to GBP1-mediated restriction of Shigella motility.

      Their study was prompted by the intriguing observation that IFNγ priming and GBP1 coat formation fail to inhibit B. thailandensis actin-based motility in HeLa cells, yet IFNγ restricts the motility of Burkholderia in T24 cells. Further investigation revealed that IFNγ restricts B. thailandensis motility in T24 cells via both GBP1-dependent and -independent mechanisms, suggesting that HeLa cells lack a critical GBP1 co-factor required to inhibit actin tail formation.

      To identify the GBP1-independent mechanism, the authors performed an siRNA screen of interferon-stimulated genes (ISGs) and identified GVIN1, a large IFN-induced GTPase, as essential for restricting B. thailandensis motility. To identify the GBP1-independent mechanism, perform a knockdown screen for ISGs and find that the loss of GVIN, a very large IFN-induced GTPase, results in higher actin tail-positive B. thailandensis in T24 cells. They further demonstrate that GVIN forms coats on the surface of B. thailandensis, which prevent the polar localization of BimA and thus actin tail formation. In summary, the data reveal two independent IFNγ-induced pathways that restrict bacterial motility: one GBP1-dependent and the other GVIN1-dependent, each relying on distinct host co-factors.

      Global assessment:

      This is a well-executed study that convincingly demonstrates how GVIN1 restricts the actin-based motility of B. thailandensis through the assembly of coatomers. The results are clearly described, the manuscript is easy to follow, and the data are overall compelling and well presented. I have only a few suggestions on how the manuscript could be further improved.

      We thank the reviewer for a clear summary of our findings, and we appreciate the reviewer’s thoughtful suggestions on our manuscript, which we addressed in our revised manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      GVIN/VLIG was previously described in the mouse, mostly. Why was it considered a pseudogene in humans? Some more background should be provided.

      Major genome annotation projects (Ensembl, GENCODE, and RefSeq) rely on automated pipelines that flag loci as pseudogenes when they contain features incompatible with a single canonical open reading frame. These pipelines score in-frame premature stop codons, frameshifting indels, or the absence of a full-length matching transcript or protein in reference databases. The human GVINP1 locus displays several such features when compared against a naive single-ORF model of the mouse Vlig1 mRNA, and on that basis GVINP1 was likely categorized as a pseudogene by all three consortia, an assignment that has propagated through UniProt, HGNC, GeneCards, and OMIM ever since (UniProt Q7Z2Y8; GeneCards GVINP1). Importantly, these pipelines are not designed to detect loci that are expressed from more than one ORF or that produce a full-length protein through non-canonical translation, and they do not incorporate mass-spectrometry peptide evidence, ribosome profiling, or interferon-inducible expression data. Therefore, these automated pipelines can draw incorrect conclusions, as is the case here. We have added to the Introduction to provide a bit more background, as requested.

      Is GVIN restricting the proliferation of the Burkholderia strain in the tested human cell line(s)? Is it restricting the intra-host mobility of the bacteria?

      GVIN1 limits intracellular bacterial replication, albeit only modestly (see new data in Fig. S5B). We found that although GBP1 and GVIN1 block actin tail formation through independent, non-overlapping mechanisms, control of intracellular B. thailandensis burden required both proteins: IFN-γ failed to restrict bacterial replication in either GBP1KO or GVIN1KO T24 cells, and the loss-of-restriction phenotypes were quantitatively similar between the two knockouts (Fig. S5B). Together, these data identify GBP1 and GVIN1 as the effectors of two cooperating IFN-γ–induced defense pathways that jointly control B. thailandensis.

      P 10: Remarkably, GVIN1 coatomers formed only infrequently on the surface of the O-antigen-deficient delta wbpm B. thailandensis mutant (Figure 5F). This information is not included in Figure 5F (only the quantification in E) and could be added.

      We have added representative images of the corresponding microscopy data for both WT and DwbpM in a new Figure S8B.

      Reviewer #2 (Recommendations for the authors):

      Major points:

      (1) The finding of GVIN1 as a protein is important and deserves further characterisation. For instance, it would be useful to investigate the transcripts they find in RNAseq, start sites, length of transcript, etc. There are two GTPase domains in this protein, but is either of them active as a GTPase if key GTP-binding and/or hydrolysis residues are mutated?

      We agree that biochemical and enzymatic characterization of the GVIN1 protein is an important future direction. Similarly, careful investigations of GVIN1 expression, ribosome profiling, etc. ought to be conducted. However, these represent substantial, self-contained lines of investigation, and we will address them in a dedicated follow-up studies.

      (2) Throughout the paper, the authors have only investigated the proximal steps of actin-tail formation. They do not go on to measure the impact of bacterial targeting by GBP1 or GVIN1, specifically in terms of bacterial spread cell-to-cell and replication (e.g., CFU measurements over time) and the impact on pyroptosis. Does GBP1 and/or GVIN1 targeting reduce bacterial CFUs over time?

      re: 1) bacterial burden (CFU data); 2) host cell death (pyroptosis), and 3) cell-to-cell spread: 1) Our revised manuscript now provides CFU data (Fig. S5B) that identifies a modest impact on bacterial burden by GVIN1 expression – please, see also our response to Reviewer #1. 2) We already provided data in our original submission (Fig. S2D) demonstrating that IFNγ priming of either T24 WT or GBP1<sup>KO</sup> cells did not appear to change the degree of host cell death in infected cells, as measured by Lactate Dehydrogenase (LDH) release assays. Accordingly, no further studies into host cell death pathways seem warranted at this time. 3) B. thailandensis is unusual among cytosolic bacterial pathogens in that it can spread between host cells not only through actin tail–driven propulsion but also via flagellar motility (PMID: 21730143). Therefore actin-based motility is not essential for cell-to-cell spread for this specific organism (PMID: 21730143). Consistent with this dual-mode dissemination, IFN-γ priming of WT T24 cells had no measurable impact on plaque-based cell-to-cell spread (new data in Fig. S5A), despite sharply reduced actin tail formation inside IFN-γ primed WT T24.

      (3) The enumeration of microscopy could be improved by providing the % of GBP1+ve or GVIN1+ve bacteria that are actin-ve.

      We added those data presentations as new panels Fig. S2G for GBP1 and Fig. S7B. As expected, these data show that under IFN-γ primed conditions GBP1-coated or GVIN1-coated bacteria fail to form actin tails.

      (4) The authors show a schematic of GBP1 and GVIN1 targeting the same bacterial cell, but they have not demonstrated this experimentally. For many of their claims, it is important to show GVIN1 and GBP1 co-staining during infection with WT and the wbpM mutant Burkholderia.

      The schematic depicted GBP1 and GVIN1 on opposite sides of the same bacterium purely for illustrative clarity and was not intended to convey that GBP1 and GVIN1 necessarily coat the same bacterial cell. We agree, however, that whether GBP1 and GVIN1 coatomers can co-decorate a single bacterium is a mechanistically interesting question, and we have now addressed it experimentally. Dual immunostaining in IFNγ-primed cells showed that most targeted bacteria were encased by either a GBP1 or a GVIN1 coatomer, but not both (Fig. S7C). A small subpopulation of GBP1/GVIN1 double-positive bacteria was nonetheless detectable (Fig. S7C–D), indicating that the two coat types are largely, but not strictly, mutually exclusive. The rarity of dual-coated bacteria reinforces our conclusion that the two coatomers operate as predominantly independent effector systems, consistent with their ability to restrict actin tail formation independently of one another.

      (5) The authors make points about GBP1 and GVIN1 as being independent pathways and the need for a cofactor. To be certain about these conclusions, have they co-expressed both GBP1 and GVIN1 together in double knockout HeLa & T24 and assessed outcomes? They started out to look for a GBP1 cofactor that is required in HeLa cells, but they found GVIN1, which seems to operate independently of GBP1 (which is not inherently an issue, but I feel this needs to be clarified better). In T24 cells, expression of (mCh-)GBP1 alone without IFNg priming does reduce Actin+ve bacteria (Figure 2C, green bars), so GBP1 can use a factor that is expressed in naïve cells? But the authors have not shown that this factor is GVIN1. The same is true for GVIN1 expressed without IFNγ in T24-GVIN1-/- (Fig 5C). What happens in a similar experiment with an O-antigen mutant?

      We believe the individual experiments the reviewer proposes have, in fact, already been performed in some fashion and are presented in the manuscript. Because these experiments are distributed across several figures, we have taken this opportunity to summarize the logic here and to point the reviewer to the relevant panels.

      First, we would like to clarify the origin of GVIN1 in our study, as the reviewer's framing suggests that we set out to identify a GBP1 co-factor and instead landed on GVIN1. Our siRNA screen (Fig. 4A–B) was in fact designed to identify ISGs required for the GBP1-independent anti-actin-tail pathway and was therefore conducted in a GBP1<sup>KO</sup> T24 background. GVIN1 emerged as a factor whose depletion restored actin tail formation in the absence of GBP1, and it is therefore, by design of the screen, an effector of a GBP1-independent pathway rather than a candidate GBP1 co-factor.

      Second, we show that single deletion of either GBP1 or GVIN1 produces a partial and quantitatively similar de-restriction phenotype, whereas only the DKO fully abolishes IFNγ-mediated restriction of actin tail formation (Fig. 4C). Thus, we are seeing the classical additive signature of two non-redundant pathways acting on the same phenotype. In the gain-of-function direction, ectopic re-expression of GVIN1 in the DKO background restores IFNγ-dependent restriction to a level indistinguishable from the GBP1<sup>KO</sup> single knockout (Fig. 5D). A reciprocal experiment co-expressing both GBP1 and GVIN1 in the DKO would only test for additivity of two effects we have already shown to occur independently and would not provide an orthogonal test of independence beyond what Fig. 4C and Fig. 5D already establish.

      Third, we do not think GVIN1 is a likely candidate for the basal GBP1 co-factor that the reviewer infers from Fig. 2C. GVIN1 is IFNγ-inducible and effectively absent from unprimed T24 cells (the putative co-factor, while IFNγ-inducible, would be expected to be expressed at appreciable levels in naïve cells). Indeed, GVIN1 entered our screen precisely because of that inducibility (Fig. 4A and RNA-seq data in Fig. S3A–B). More decisively, we show that IFNγ-primed HeLa cells allow for ectopically expressed GVIN1 but not for ectopically expressed GBP1 to restrict actin tail formation. In other words, the ISG co-factor for the GVIN1 pathway must be distinct from the ISG co-factor for the GBP1 pathway. Lastly, we now show that GBP1 and GVIN1 coatomers are largely mutually exclusive, further arguing against GVIN1 acting as the GBP1 co-factor or vice versa in blocking actin tail formation.

      Fourth, the O-antigen mutant experiment the reviewer asked about has also been performed and provides another point of mechanistic separation between the two pathways. Ectopically expressed GVIN1 assembles a coatomer on WT B. thailandensis but only rarely on the ΔwbpM O-antigen-deficient mutant (Fig. 5F). This observation parallels (and thus mechanistically explains) why ΔwbpM is resistant specifically to the GBP1-independent (i.e., GVIN1-dependent) branch of IFNγ defense (Fig. 3E). GBP1, by contrast, engages both B. thailandensis WT and the O-antigen ΔwbpM mutant efficiently (new Fig. S8C).

      Taken together, four convergent lines of evidence (additive genetics in the DKO (Fig. 4C), cross-complementation of GVIN1 in the GBP1-null background (Fig. 5D), non-overlapping co-factor requirements in HeLa versus T24 cells (Fig. 2, Fig. 5), and distinct O-antigen dependencies for the two coatomers (Fig. 5E, Fig. 8C)) establish that GBP1 and GVIN1 constitute two parallel, independently operating antimicrobial modules, each likely with its own set of IFN-inducible co-factors.

      (6) The discussion does not currently discuss GBP1/Caspase-4-driven pyroptosis as a host-defence mechanism, which should be included. The authors should also discuss why Shigella and Burkholderia are differentially targeted, given the differences in the mechanism of actin polymerisation by IcsA and BimA.

      We previously showed that a GBP1 polybasic motif mutant that is unable to form coatomers on bacteria remains able to activate Caspase-4-driven pyroptosis and thus demonstrated that coatomers are not strictly required for GBP1-dependent Caspase-4 activation (PMID: 37023136). We now provide this information in the Discussion of the revised manuscript.

      We provide new data showing that GVIN1 forms coatomers on BimA-deficient and WT B. thailandensis with similar efficiency (new Fig. S8A) and we previously demonstrated that icsA plays no role in GBP1 coatomer formation (PMID: 33885766). Therefore, actin polymerization appears to play no role in either GVIN1 or GBP1 coatomer formation.

      Minor points:

      (1) Have the authors used anti-actin antibodies or phalloidin staining? Please clarify.

      We used phalloidin staining and this information is now also provided in the Results section (lines 118-119) in addition to the Materials and Methods section of the paper.

      (2) Provide the Gene ID, Ensembl ID, etc., for GVIN1 at first mention.

      We inserted the text: “Because automated annotation pipelines flag predicted in-frame stop codons, GVIN1 has long been catalogued as the pseudogene GVINP1 (GTPase, Very large IFN-Inducible Pseudogene-1) by Ensembl, GENCODE, and RefSeq (HGNC 25813; NCBI Gene 387751; OMIM 616121).” in the Introduction (lines 104 – 107)

      (3) The bond linking the core sugars with the O-antigen is missing in the schematic of LPS in Figure 3A. Please mention which sugars these are, if it is known for Burkholderia.

      We have corrected the schematic (Fig.3A) to include the bond linking the core sugars with the O-antigen.

      (4) RNA-seq datasets should be deposited to GEO or similar public databases, and project IDs provided.

      The RNA-seq datasets was deposited to the NCBI Gene Expression Omnibus (GEO), and the project accession IDs are is cited in the Data Availability statement.

      (5) The description of the transcriptomics analyses was not clear - are the heatmaps in Figure S3 of genes that were induced by IFNg in these cell lines, but not HeLa or A594, but the description states fold-change over naïve cells?

      The reviewer is correct and we apologize for the error in the figure legends, which we edited to clarify that the heatmap compares gene expression across IFNγ-primed cell lines.

      Reviewer #3 (Recommendations for the authors):

      Major comments:

      (1) It would be nice to include some additional analysis on GVIN: GBP1 and GVIN1 seem to recognize different structures and need different cofactors. Are they structurally similar (alignment)? Can you map functionally important GBP1 regions on GVIN1, such as a polybasic motif, similar to motifs found in GBP1? Or does GVIN have a lipidic membrane anchor or a CAAX box? In other words, is GVIN binding directly to the outer membrane or through the unknown cofactor? Is GVIN an effector of another protein that binds to the OM?

      Detailed structural studies including the mapping and biochemical characterization of GVIN1's membrane-targeting domain(s) represent substantial, self-contained lines of investigation that are beyond the scope of this study and that we hope to present in a future follow-up study.

      (2) In T24 cells, GVIN1 and GBP1 (and their cofactors) get induced upon IFNg priming. Upon B. thailandensis infection, is it possible to see mixed or double coats? Does GBP1 have a higher affinity for Shigella and GVIN for B. thailandensis? Is GVIN1 B. thailandensis-specific? It would be a nice addition to discuss other virulent Burkholderia species that make actin tails (such as B. pseudomallei) to further enhance the possible relevance of GVIN1.

      Please see our response to reviewer #2: dual immunostaining in IFNγ-primed cells showed that most targeted bacteria were encased by either a GBP1 or a GVIN1 coatomer, but not both (Fig. S7C). A small subpopulation of GBP1/GVIN1 double-positive bacteria was nonetheless detectable (Fig. S7C–D), indicating that the two coat types are largely, but not strictly, mutually exclusive. Re: the measurement of GBP1/ GVIN1 affinities for different bacteria – we agree that this is an area of future exploration but not within the scope of this study. Extending research on GVIN1 to other bacterial organisms including other Burkholderia species is indeed among our future research goals. We more broadly speculate on the potential importance of GVIN1-mediated host defense targeting other Burkholderia species in the Discussion (see lines 363 ff)

      (3) In panel F of Figure 6, It appears that GVIN forms full coats in the absence of IFNγ priming, but relocalizes to the bacterial poles upon IFNγ treatment. Is this shift in localization a representative and consistent observation, or could it be an artifact of the specific image selected? Additionally, could this relocalization be driven by the presence of IFNγ-induced cofactors? For example, might GVIN interact directly with BimA, or could an IFNγ-dependent cofactor mediate its recruitment to the poles?

      We believe the author is commenting on Figure 5F instead of 6 based on the question. The perceived shift in GVIN1 localization to the bacterial pole under IFNγ-primed conditions was not something we routinely observe and the original image shown in Fig. 5F is likely an outlier. We therefore replaced the original image with a more representative image.

      (4) The authors conclude that BimA is eliminated from the outer membrane, but it is unclear what evidence this conclusion is based on. Could it be that BimA is not eliminated, but rather becomes diffusely distributed in the membrane in the presence of GVIN1, and is therefore more difficult to detect by microscopy? One way to test this hypothesis would be to delete BimC. If BimA localization is indeed dependent on BimC, its signal should become diffuse. If a BimA signal can be detected in BimC-deficient bacteria, but not in GVIN-positive bacteria, it would indeed strengthen the claim that BimA is removed from the membrane.

      Prior literature demonstrates that on a bimC mutant, BimA remains detectable on the bacterial surface in a dispersed manner and that this dispersed BimA localization correlates with an "actin cloud" surrounding the bacteria. In our GVIN1-coated bacteria, we do not detect an actin cloud or residually localized BimA signals. We have now mentioned this in the Results section line 305 - 308 referencing this published phenotype to strengthen our argument that BimA is likely cleared from the membrane rather than merely redistributed.

      (5) The siRNA mini-screen would benefit from appropriate controls demonstrating that the targeted genes were effectively knocked down. Without this, it's difficult to interpret negative results with confidence. It remains possible that the co-factor of GVIN1 is among the ISGs tested, but that the knockdown was insufficient to produce a detectable phenotype.

      We now acknowledge this limitation. We have updated the text on lines 221-223 to explicitly state that our mini-screen does not rule out potential false negatives due to varying knockdown efficiencies.

      (6) The authors convincingly demonstrate that GVIN1 coats prevent actin tail formation by B. thailandensis, but they do not explore the broader biological consequences of this observation. An important next step would be to assess whether GVIN1 coating restricts B. thailandensis cell-to-cell spread. By 8 hours post-infection, a clear difference in spread should be observable if actin-based motility is impaired. Additionally, it would be valuable to determine whether GVIN1-coated bacteria are still capable of intracellular replication. These functional assays would provide important insight into the biological relevance of GVIN1-mediated coating.

      We have now assessed the effect of GVIN1 on intracellular bacterial replication (see new CFU data in Fig. S5B) and cell-cell motility (see new data in Fig. S5A). Please, also refer to our comments in response to Reviewer 1 for more details.

      (7) As far as I am aware, the exact function of the GBP1 coat remains unclear. The authors refer to their earlier work (Kutsch et al., 2020) and claim to have demonstrated an "emulsifying activity." However, this overinterpretes the findings of that publication, since Kutsch et al. (2020) showed that the GBP1 coat disrupts the barrier function of the O-antigen, rather than compromising the integrity of the outer membrane itself. This disruption increases bacterial susceptibility to polymyxin B, but does not show an 'emulsifying effect'. Furthermore, the same group later reported that GBP1 exhibits an "aggregating" activity on LPS micelles (Dickinson et al., 2023), which seems to contradict the notion of an emulsifying function. I would recommend tempering the current claims about GBP1's activity. The precise impact of the GBP1 coat on the bacterial outer membrane remains unresolved and should be presented as an open question.

      Our previous work demonstrated GBP1 coatomers on the bacterial surface disrupt the membrane-stiffening and membrane-shielding activities of O-antigen. We stand by the interpretation of our results and think it is likely that GBP1 coatomers act like surface-active agents or emulsifiers. However, we agree with the reviewer that more biophysical studies are needed to definitively test this model. Accordingly, we have revised some of the language and clearly articulated that GBP1 coatomers acting as a surfactant is a model that we are proposing.

      As the reviewer correctly stated we have also shown that GBP1 can aggregate “free” LPS, i.e. to act more like a flocculant. Substances that are both flocculants and surfactants exist (e.g. long-chain fatty acid soaps). However, these substances typically do not act as flocculants and surfactants simultaneously under the exact same conditions. Similarly, we propose that GBP1’s role depends on context: we propose that GBP1 coatomers on the bacterial surface act as surfactants, while polymerizing GBPs bound to LPS acts a flocculant.

      Minor comments:

      (1) Figure 3: Can a GBP1 coat assemble on the delta wbpM mutant of B. thailandensis? The authors suggest GBP binds to the O-antigen, but other publications suggest that the O-antigen is dispensable in certain bacteria.

      We conducted these experiments – see also our response to reviewer #2. Briefly, ectopically expressed GVIN1 assembles a coatomer on WT B. thailandensis but only rarely on the ΔwbpM O-antigen-deficient mutant (Fig. 5F). GBP1, by contrast, engages both B. thailandensis WT and the O-antigen ΔwbpM mutant efficiently (new Fig. S8C). We do not argue that GBP1 requires O-antigen binding for coatomer formation on Burkholderia. However, direct or indirect interactions between GVIN1 and O-antigen may be required for GVIN1 coatomer formation.

      (2) Figure 4: GBP6 appears as a hit in the T24 screening but turned out to be a false positive. How does that look in the other cell lines that were part of the screen (U937, THP1)?

      the characterization of T24 GBP6<sup>KO</sup> cells clearly demonstrated that GBP6 is dispensable for either the GBP1 or the GVIN1 pathway in these cells. While we cannot formally rule out that GBP6 would be required for the GBP1/GVIN1 pathways in other cell lines, there are no compelling arguments in favor of this model – as far as we can tell - and we therefore did not investigate this further.

      (3) Figure 5F: Please show the blots confirming exogenous GVIN expression.

      We have provided an immunoblot showing the expression of exogenous GVIN1in HEK293T cells in new Fig. S7A

      (4) Figure 6: Can you induce GVIN expression after actin tails have been formed and stop disassembling them?

      This is an interesting idea and will require a technically challenging video microscopy approach that we will consider for future investigations.

      (5) Figure 6: Are the bacteria showing no BimA signal GVIN1-coated? Eventually, stain for LPS to see if bacteria are lacking BimA also lack LPS.

      Our preliminary studies indeed support this and thus indicate that the elimination of BimA from the bacterial surface requires the concerted effort of the GVIN1 coatomer and its putative co-factor (or the GBP1 coatomer and its putative co-factor, respectively). A detailed characterization of the mechanism by which BimA is removed from the bacterial surface will be part of future studies.

    1. Test Mode lets you run your whole integration against simulated banks, card networks and NPCI before you take real payments.

      We should also mention - Razorpay Testing Guide helps enginners team and agents write test cases that help test various edge cases in integration

    1. eLife Assessment

      This study represents an important contribution to the study of decision-making under risk, bringing an interdisciplinary approach spanning economic theory, behavioral neuroscience, and computational modeling to test how choice preference is influenced by rare and extreme events. Using a novel experimental design, the authors present evidence that rats are indeed sensitive to these rare and extreme events despite their infrequent occurrence, driven primarily by an almost complete avoidance of "Black Swans" - rare and extreme losses. Motivated by economic theory, computational models of the choice behavior provide solid evidence of a distinct distributional effect, in which rare outcomes are treated as a differently-weighed contributor to the overall expectation associated with each option. Noting the inevitable difficulty in empirically testing the impact of rare events in animal tasks, these results bring a novel interdisciplinary perspective to a question of broad interest for understanding decision-making in complex probabilistic environments through the lens of economic choice.

    2. Reviewer #1 (Public Review):

      [Editor's note: this version has been assessed by the Reviewing Editor without further input from the original reviewers, given the substantial revisions completed since the original submission: these have improved the clarity and transparency of the results in a relatively complex study. These changes largely address those concerns raised in review that do not relate to the underlying experimental design and the control of the distinct task conditions.]

      Summary:

      In this manuscript, the authors investigate the impact of rare and extreme events on rodents' decision-making under risk, in gain and loss contexts. They describe the behavior of 20 rats performing a four-armed bandit task, where probabilistic gains (sugar pellets) and losses (time-out punishments) can - in some arms - incorporate extremely large - but rare - outcomes. They report that most rats are sensitive to rare and extreme outcomes despite their infrequent occurrence, and that this sensitivity is primarily driven by extreme loss events which they try to avoid, rather than extreme gains that they seek to obtain.

      They finally propose a modification of standard reinforcement-learning, which features a specific sensitivity to rare and extreme outcomes and can account for the observed behavior.

      Strengths:

      The manuscript really taps into a surprisingly neglected but very relevant aspect of decision-making: the effect of rare and extreme events (REE). The authors have developed an experimental setup that seemingly allows investigation of this aspect, which is not trivial given the idiosyncratic properties of rare and extreme events.

      The parameters of the experimental setup seem also to be well thought off: basically, in the absence of REE, some options are objectively better than others (because, in expectation, they overall deliver more food, or minimize time-out punishments), but this ordering reverses if REE are taken into account. This allows for a clean test of the integration of REE in the rodent's decision-making model.

      The data is presented and analyzed in a very descriptive but exhaustive and transparent way, down to the description of individual rodent's behavior.

    3. Reviewer #2 (Public review):

      Summary:

      This paper attempts to examine how rare, extreme events impact decision-making in rats. The paper used an extensive behavioural study with rats to evaluate how the probability and magnitude of outcomes impact preference. The paper, however, provides limited evidence for the conclusions because the design did not allow for the isolation of the rare, extreme events in choice. There are many confounding factors, including the outcome variance and presence of less-rare, and less-extreme outcome in the same conditions.

      Strengths.

      (1) The major strength of the paper is the significant volume of behavioural data with a reasonable sample size of 20 rats.

      (2) The paper attempts to examine losses with rats (a notoriously tricky problem with non-human animals) by substituting time-outs as a proxy for losses. This allows for mixed gambles that have both gain and loss possible outcomes.

      (3) The paper integrates both a behavioural and a modelling approach to get at the factors that drive decision-making.

      (4) The paper takes seriously the question of what it means for an event to be rare, pushing to less frequent outcomes than usually used with non-human animals.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #2 (Public review):

      Summary:

      This paper attempts to examine how rare, extreme events impact decision-making in rats. The paper used an extensive behavioural study with rats to evaluate how the probability and magnitude of outcomes impact preference. The paper, however, provides limited evidence for the conclusions because the design did not allow for the isolation of the rare, extreme events in choice. There are many confounding factors, including the outcome variance and presence of less-rare, and less-extreme outcome in the same conditions.

      Strengths.

      (1) The major strength of the paper is the significant volume of behavioural data with a reasonable sample size of 20 rats.

      (2) The paper attempts to examine losses with rats (a notoriously tricky problem with non-human animals) by substituting time-outs as a proxy for losses. This allows for mixed gambles that have both gain and loss possible outcomes.

      (3) The paper integrates both a behavioural and a modelling approach to get at the factors that drive decision-making.

      (4) The paper takes seriously the question of what it means for an event to be rare, pushing to less frequent outcomes than usually used with non-human animals.

      Weaknesses:

      (1) The primary issue with this work is that the primary experimental manipulation fails to isolate the rare, extreme events in choice. As I understand the task, in all the conditions with a rare extreme event (e.g., 80 pellets with probability epsilon), there is also a less-rare, less-extreme event (e.g., 12 pellets with probability 5). In addition, the variance differs between the two conditions. So, any impact attributable to the rare, extreme event could be due to the less rare event or due difference in the variance (or other statistical moments, like skew or kurtosis). That the distributions can be shown to be different under specific assumption to value maximizing agents (e.g., with Jensen Gaps and Table 2) is not really relevant to what rats are sensitive and what drive their behaviour. The design here does not support the conclusions. Finally, by deliberately confounding rarity and extremity, the design does not allow for assessing the impact of either aspect on rat behaviour.

      This is a repetition almost word for word of point (1) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (2) The RL modelling work also fails to show a specific impact of the rare extreme event. As best as I can understand Eq 2, the model provides a free parameter that adds a bonus to the value of either the two options with high-variance gains (A and V in the paper) or to the two options with high-variance losses (F and V in the paper). Or equivalently to the ones with "Jackpots" vs the ones with "Black Swans" (see Point 1 above as to how these different aspects are all confounded in this design). This parameter seems to only depends on whether this option could have possibly yielded the rare, extreme outcome (i.e., based on the generative probability) and was not connected to its actual appearance. [This point is unclear as the text says this, but the rebuttal states otherwise; plus some options never received the REE, see Table S11]. That makes it a free parameter that just bumps up (or down) the probability of selecting a pair of options. That may be due to presence of the REE or the other rare event or just the variance difference. Moreover, in the case of the "black swan" or high-variance loss conditions, this seems very much like a loss aversion parameter, but an additive one instead of a multiplicative one. Is there a theoretical claim here that "extreme losses" need an additive loss-aversion parameter?

      This is a repetition almost word for word of point (2) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (3) The paper presented the methods and results with lots of neologisms and fairly obscure jargon (e.g., fragility, total REE sensitivity). That might it very hard to decipher exactly what was done and what was found. For example, on p. 4, the use of concave and convex was very hard to decipher; the text even has to repeat itself 3 times (i.e., "to repeat" and "in other words") and is still not clear. It would be much clearer (and probably accurate) to say that the options varied along the variance dimension, separately for gains and losses. Option A was low-variance gains and losses. Option B was low-variance losses and high-variance gains. Option C was high-variance losses and low-variance gains, and Option D was high-variance losses gains. That tells much more clearly what the animals experienced without the reader having to master a set of new terminologies around fragility and robustness, which brings a set of theoretical assumption unnecessarily into the description of the experimental design. Alternatively, if the authors are wary of using the term "variance" because other moments of the distribution also differ, they could use "high-value gains" or "high-value losses" or something else which does not obscure the experimental design with jargon. Again, this goes back to point 1 above, whereby the different options differ on so many dimensions (as is made even more apparent in the rebuttal) that the design cannot isolate the impact of the variables of interest.

      This is a repetition almost word for word of point (3) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space. The new words that we use are an attempt at reflecting the novelty of the approach and design.

      (4) Were the probabilities shuffled or truly random (seem to be fixed sequences, so neither)? What were the experienced probabilities? Given the fixed sequences, these experienced ("ex-post") probabilities, could differ tremendously from the scheduled ("ex ante") probabilities. It's quite possible than an animal never experienced the rare, extreme event for a specific option. From Table S11, that is guaranteed to have happened in that 4 animals only ever experienced the "black swan" outcome once. It's even possible (if they only picked a specific option on the 10th/60th choices by chance), that they only ever experienced that rare extreme event. This point still cannot be known given the information provided, which does not break down outcomes by options. The Supplemental in Table S11 only gives overall numbers but does not indicate what the rats experienced for each choice/option-which is what matters here. A simple table that indicates for each of the 4 options, how often they were selected, and how often the animals experienced each of the 6-8 possible outcome would make it much clearer how closely the experience matched the planned outcomes. In addition, by restricting the rare outcome to either the 10th or 60th activations in a session, these are not random. Did the animals learn this association? The text states that they did not, but no evidence is provided.

      This is a repetition almost word for word of point (4) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (5) The choice data are generally presented in an over-processed fashion with a sum and a difference (in both figures and tables). The basic datum (probability/frequency of selecting each of the 4 options) is not provided directly in the main text, even if it can theoretically be inferred from the sum and the difference. New right side of Table S4 is probably the most valuable piece in terms of explaining what rats did and should be highlighted a lot more. Inspection of that table reveals some interesting (and potentially worrying) results. Most notably, the vast majority of responding happens on the "anti-fragile" and "robust" option, often totalling around 90% of all selections, especially amongst the most common blue rats. Alas, those were all those the two options that were deliberately assigned to the two most preferred holes in the training phase (see p. 26). Does this reflect genuine preference for reward distributions or does this reflect a spatial hole bias? The assignment strategy makes this impossible to tell apart.

      This is a repetition almost word for word of point (5) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space. In addition, in Figure 8 of Material and Methods, we report the changes in behavior between the training and final sessions. The related discussion stresses that half of the rats changed more than 30% of their choices in the final experimental sessions compared to the training sessions. See also our previous reply to point (8).

      (6) There is insufficient detail provided on the inferential statistical tests (e.g., no degrees of freedom or effect sizes), and only limited information on exactly what tests were run and how (bootstrapping, but little detail). Without code or data (only summary information is provided in the supplement), this is difficult to evaluate. In addition, the studies seem not to pre-registered in any way, leaving many research degrees of freedom. Not all studies need to be pre-registered and sometimes discovery of new things requires exploratory work, but preregistration does provide additional safeguards against overemphasizing post-hoc detected patterns-a serious issue in behavioural science. Moreover, this promotes transparency in reporting results and analyses, allowing for a better assessment of the strength of evidence for a claim. For example, here, were any alternative analysis pipelines attempted? Also, there were many sub-groupings of the animals and subsequent comparisons between them which all seemed post-hoc. On what grounds were these divisions made-were other divisions examined as well?

      This is a repetition almost word for word of point (6) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (7) On p. 12 (Fig 4), there is an attempt to look at the impact of a rare, extreme event by plotting a measure of preference for the 10 trials before/after the rare, extreme event. In the human literature, the main impact of experiencing a rare, extreme event is what is known as the wavy recency effect (See Plonsky et al. 2015 in Psych Review for example, now cited). What this means is that there tends to there tends to be some immediate negative recency (e.g., avoiding a rare gain) followed by positive recency (e.g., chasing the rare gain). Typically, this refers to the specific option that yielded that outcome. First, as the other analyses do, the current analysis combines choice of the option that yielded the rare outcome with choice of other options, so that cannot directly assess the impact of the rare, extreme event on choice. Also, using a 10-trial window would thus obscure any impact of this rare, extreme event. There is mention of the very next trial, but an analysis that looks at the 10-trial time course trial-by-trial could reveal any impact that might be predicted from the human literature.

      This is a repetition almost word for word of point (7) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (8) As I understood the method (p. 31), the assignment of options to physical locations was not random or counterbalanced, but deliberately biased to have one of the options in the preferred location. This would seem to create a bias towards a particular option and a bias away from the other options, which confounds the preference data in subsequent analyses. Table S4 reinforces this concern where the vast majority of response are clustered in the two most preferred options from training.

      This is a repetition almost word for word of point (8) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (9) Are delays really losses? This is a big assumption. Magnitude and delay are different aspects of experience, which are not necessarily commensurable and can be manipulated independently. And, for the model, how were these delays transformed into outcomes for the model. Eq 1 skips over that. Is there an assumption of linearity? In addition, I was not wholly clear if the delays meant fewer trials in a session or if the delays merely extended the session and meant longer delays until the next choice period.

      This is an exact repetition of point (9) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      Other points:

      (1) I think the authors still misunderstand the concept of "hot-stove effects". The idea is that the experience of a very bad outcome can lead to avoiding the situation again (i.e., not sampling that option) and can provide the appearance of oversensitivity to that bad outcome. Here, that might be more thought as "black-swan avoidance". Imagine if, to the rat, all options are equal in value, then some initial bad luck in encountering the black swan might make the animal avoid that option, even though with enough experience, then it would have been equal in value.

      This point is reminiscent of recommendation (5) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (2) I am still not convinced that the Jensen inequalities add to this paper in terms of understanding the rat behaviour. That may be more suited for a different paper about the statistical and mathematical properties of certain generative distributions, but not here given what rats actually choose and experience.

      This point is reminiscent of recommendation (8) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (3) Providing the data open access is very good. The code, however, should be equally available and not just upon request. Code needs to be available for assessment during peer review and for reproducibility checks. There are substantial enough problems with reproducibility in the field that code availability should be a minimum criterion for publication (see Miske et al., 2026 in Nature for the most recent large-scale evaluation of this problem).

      This point is reminiscent of recommendation (9) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (4) The paper still somewhat mischaracterizes the literature on rare events, posing it as a series of "exceptions", rather than recognizing that a huge chunk of the literature uses rare events rarer than 10%. Also, there is even existing terminology in that literature for exactly the situation that is being created here-rare treasures (aka jackpots here) and rare disasters (aka Black Swans here).

      This is reminiscent of point (10) appearing in your previous report, which we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (5) Defining the observed behaviour in terms convexity, instead of stating choices more plainly obscures what is done/found. This is especially the case here because convex and concave mean different things when applied to gains/losses in terms of whether or not that option can lead to the REE. The use of the terms obscures rather than clarifies and probably is best left for the discussion (and maybe the intro) when mapping from theoretical distributions to the experiment at hand. In the paper, even the bottom of p.5 seems to incorrectly define "Total Sensitivity" as the combined proportion of selecting convex options in either domain, which does not map how convex is defined in Fig 1B or elsewhere in the text.

      This is reminiscent of point (3) appearing in your previous report, which as stated above we have addressed at length in our first-round reply. Therefore, we do not reiterate here to save space.

      (6) Fig 1C is baffling. Why are probabilities drawn moving away from the origin? The standard scientific plotting convention is for numbers to grow when moving away from the origin. That would be vastly clearer. Also, the color coding is confusing. Green-red maps onto convex-concave, but that would naturally seem to indicate gains vs losses, not convex vs concave. And why are probabilities growing larger in both directions from the origin? Much more sensible to communicate the procedure would likely be a standard plot of magnitude vs probability.

      As explained in the main text of the article, panel (c) in Figure 1 reveals an important and novel feature of the design: gains and losses are either convex or concave in decreasing probabilities, meaning that the gain/loss gets more rare, it becomes larger (if convex) or smaller (if concave) in a nonlinear fashion.

      (7) Discussion: I think the main difference between the human situations discussed and this experiment is that humans have not experienced those rare "black swan" outcomes. Rather, they hear about the disasters that are possible and do not incorporate that information, as discussed in the description-experience literature already cited in this paper (though not in that context).

      We thank the referee for pointing this out. However, it is not clear to us that the various trauma experienced in real life by humans do not share properties with Black Swans in our experimental design.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Small points:

      - p. 4. Options are not strategies. Picking two out of the 4 options more often, is not picking two strategies. That's picking two options (also on p. 7, but really throughout).

      This has been changed in the text.

      - The sums don't add up in probability of outcomes. How were they 0.9, 0.1, 0.01? What were they exactly? What exactly is epsilon?

      Exact probabilities, which of course add up to one, are given in Material and Methods (page 29).

      - The term "one-sided sensitivity" to refer to the selection of options include rare-extreme outcomes is also confusing. There are many other words that might work better here, including "encounter", "choice", "selection", or even "preference".

      One-sided Sensitivity if a proposed measure of the choice asymmetry, as explained in the text (pages 5-6, including caption of Figure 1).

      - I don't think that Figure 3 is more intuitive. It's still a summed measure, with rats sub-divided into post-hoc groups. Also, aren't the groups divided precisely in part by the selections that are being shown in this plot?

      There is no claim that Figure 3 is more intuitive. As explained in Material and Methods (pages 33-34), it provides an alternative way to represent behaviors (BSA/JPS vs TSREE/OSREE).

      - Fig 3 has no black or dark and light grey. There is no one asterisk in the figure. The light grey is irrelevant because all bars must sum to 1. No error bars or equivalent on the plot.

      Dark and grey bars sum to one but have a different interpretation, as explained in the text (pages 10-11). We have dropped the one asterisk reference.

      - p. 14. Technicality, but alpha need not be bound at 1 for a convergent equation. It only needs to be bound at 2 (real maximum value). Above 1 (but below 2) would be a type of overcompensation that would also converge at same Q value.

      The assumption that alpha is positive and bounded by one is common in the literature and is important for stochastic convergence. Assuming that alpha is between one and two has undesirable implications, beyond possible non-convergence, in particular that Q values may fluctuate.

      - p. 24. What was the generative frequency (not experienced frequency). Not adequately explained. Less than half a percent of all outcomes? Of all times that the relevant choice was made. This is not explained.

      The sequences used to generate gains and losse are detailed in the Supplementary Material.

      - p. 4. Says 41 sessions, but methods say 40 (e.g., Fig 7 and surrounding text)

      This has been corrected in the text.

      - My understanding of Tables S5 and S6 is that the best models did not include REE in Q sub-values. That seems to be opposite to the textual conclusion.

      As stated in Table 1 (page 15), which summarizes results in Tables S5-S6, the selected models for rats 3, 11, and 12 do not integrate REE while the selected models for rats 4, 16 and 20 integrate REE though Q values and REE decision weights. For most rats, REE are integrated in the selected models through the latter only.

      - What are the parameters in Table S7? The full 8-parameter model? But how are these the ones selected by BIC as per caption? Also, there are a number of anomalous values-lots of 1s and 0s, plus a +40 on the value of the losses for Rat 20. Columns are always gain then loss except first column.

      We report in Table S7 all estimated parameters for each rat, but only for the model selected by the BIC criterion (see Table S5). Admissible values for alphas include 0 and 1. All estimates have been checked for identification. The first and second columns have been switched to have gain first.

      - Table S11 does not subdivide by option selected. So, we cannot tell if the rats actually experienced the outcome distribution associated with some options.

      Given the large number of nosepokes generated by each rat, reporting all actual gains and losses would consume too much space. On the other hand, given that each option has only three frequent and rare values, it is expected that each will be experiences by each rat, in view of the choice frequencies reported in Table S2 (page 52).

    1. eLife Assessment

      The study provides an important advance towards understanding how spatial and temporal transcriptional programs are integrated to regulate lineage-specific chromatin and enhancer activation. The functional evidence is currently based on limited evidence, but the current data provide a solid correlative and conceptual foundation. Functional experiments directly linking Gsb occupancy to chromatin state and regulation of some lineage-specific targets would further strengthen the causal interpretation of the model. Clarifying the scope of conclusions and explicitly acknowledging the technical limitations of current chromatin assays would provide a more balanced interpretation of the manuscript.

    2. Reviewer #1 (Public review):

      The authors did very little to address our concerns outside of written clarifications. While they altered wording to describe Gsb from "pioneering" to "chromatin priming" our stance remains that there is no evidence shown to confirm that Gsb plays a role in chromatin remodelling. (IE while ectopic expression of Gsb results in a different chromatin conformation, there is no evidence that the ectopic expression of Gsb alters an already established chromatin state.) Additionally, the phenotypic experiments show loss of NB5-6 reporter and neuronal markers upon loss of Gsb which confirmed the developmental requirement for Gsb within the endogenous NB5-6 domain, but another striking feature that was described was that loss of Gsb also results in loss of NB3-5 identity. The abrupt change to a new NB lineage made this portion of the manuscript feel a bit disjoined and while this is described as an additional reciprocal phenotype, there are still no sequencing data or other evidence as to how this loss has occurred.

      We disagree that pioneer or chromatin-priming activity should be inferred strictly from single timepoint binding profile. We also disagree that any "remodeling" was shown. To sufficiently show remodeling, one must express a factor at/after a time where the chromatin conformation has been established to be already open/closed and then show the expression of that factor induces conformation changes to the opposite conformation. Otherwise, it is hard to infer whether these chromatin changes are due to the presence of the factor blocking the normal conformational changes that should occur.

      The authors mention they walked back the terminology from "pioneering" to "chromatin priming" and make it clear that Gsb does not act alone to establish the chromatin landscape. We are not sure that this is proper terminology either.

      We mention that the phenotypic data do not align with the sequencing data. The authors added text to clarify the rationale behind each experiment - some being to induce outside of the endogenous domain with clean genetic access while others to obtain information within the endogenous spatial domain.

    3. Reviewer #2 (Public review):

      Summary:

      This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.

      Comments on revised version.

      The authors have made extensive revisions and addressed the concerns raised in my previous review. Overall, I find the revised manuscript substantially improved, both experimentally and conceptually. In particular, the additional locus-level and genome-wide analyses, the motif analysis of En occupancy, the attempted endogenous Gsb depletion, and the revised interpretations and distributed STF-code model have strengthened the manuscript considerably.

      (1) Adding Gsb occupancy profiles at specific gene loci such as lbe, ap, and eya, along with chromatin accessibility, Hb binding, and expression data at ems, enhances the link between TaDa results and lineage phenotypes. Although these data do not prove direct regulation of each locus, they strongly support a correlation between Gsb binding, chromatin state, and neural stem cell identity. Therefore, this aspect is mostly addressed, as long as the manuscript does not present these correlations as conclusive evidence of direct regulation.

      (2) We appreciate the additional genome-wide analysis distinguishing genomic loci that gain versus lose accessibility following Gsb misexpression, as well as the comparison with two independent Gsb occupancy datasets and the corresponding Hb occupancy analysis. These data strongly support the conclusion that Gsb has chromatin-remodelling capacity and that accessibility loss occurs at loci associated with Gsb occupancy.<br /> We also appreciate the authors attempt to examine endogenous Gsb function using early CRISPR depletion and their explanation of why this approach could not subsequently be coupled to lineage-specific TaDa. The inability to directly assess chromatin consequences of Gsb loss in native NB5-6 remains a limitation, but the authors have now acknowledged this appropriately. We therefore consider this comment substantially addressed.<br /> We would, however, encourage the authors to distinguish clearly between demonstrated chromatin-remodelling capacity and the stronger, physiologically unresolved claim that Gsb establishes chromatin priming in NB5-6.

      (3) The response to the question about "En not being a pioneer factor" is particularly satisfactory. The new motif analysis directly tackles the concern that the lack of En occupancy at NB5-6-specific sites might just be due to the absence of En binding motifs. The similar presence of En motifs at NB5-6- and NB7-4-specific sites strengthens the conclusion that differing occupancy is unlikely caused by motif absence. Additionally, comparing the accessibility dependence of En and Gsb occupancy helps argue against the idea that TaDa cannot detect binding at less accessible loci. Importantly, the authors have removed the categorical claim that "En is not a pioneer factor" and now limit their conclusion to the specific developmental and cellular context studied. This response adequately addresses the comment.

      (4) The revised distributed STF-code model is clearer and provides a useful framework for explaining the complementary roles of Gsb, En, and other spatial factors. I would encourage the authors to clarify that the proposed stepwise model is a conceptual framework rather than an experimentally established temporal sequence. In particular, the data do not directly demonstrate that Gsb-dependent chromatin priming is causally followed by Hb recruitment at the same loci. Similarly, the division of labour among STF components should not be interpreted as mutually exclusive, since individual STFs could contribute to both chromatin remodelling and TTF-dependent enhancer regulation. The schematic and accompanying text should therefore distinguish the demonstrated differences in Gsb and En chromatin engagement from the broader proposed model.

      (5) The authors correctly recognise that the factors determining the NB7-4 accessibility landscape are still unknown. Pinpointing Msh and other motif-enriched candidates clarifies this issue and offers valuable leads for future research.

      Overall, the authors have responded constructively to the major concerns raised in the review. The revised manuscript presents a substantially more coherent model in which STF-code components have distinct but complementary roles in shaping the chromatin landscape and enabling lineage-specific Hb engagement.

    4. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      The study provides an important advance towards understanding how spatial and temporal transcriptional programs are integrated to regulate lineage-specific chromatin and enhancer activation. The functional evidence is currently incomplete, but the current data provide a solid correlative and conceptual foundation. Functional experiments directly linking Gsb occupancy to chromatin state and regulation of some lineage-specific targets would further strengthen the causal interpretation of the model. Clarifying the scope of conclusions and explicitly acknowledging the technical limitations of current chromatin assays would provide a more balanced interpretation of the manuscript.

      We thank the reviewers and editors for their comments on our manuscript. We address here the concerns raised by them.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      It has long been known that Drosophila embryonic ventral nerve cord neuroblasts incorporate both spatial and temporal transcription factor expression to generate 30 distinct neuroblasts and lineages per hemisegment. This manuscript aims to elucidate the mechanism by which this integration of spatial and temporal transcription factors occurs through "direct regulation" or "epigenetic regulation". Direct regulation is defined as both spatial and temporal factors binding to open chromatin and working together to dictate specific lineages. Epigenetic regulation is defined as a spatial factor priming the chromatin in a neuroblast-specific manner to allow for the integration of temporal factors to generate specific lineages. The authors conclude that there is a two-step model in which a spatial transcription factor code "primes" the chromatin in terms of accessibility and then recruits temporal factors to ensure lineage-specific enhancer activation.

      We thank the reviewer for this clear and succinct summary and for accurately capturing the central idea of the model we propose. In particular, we appreciate that the reviewer highlights the distinction between the previously proposed “direct regulation” and “epigenetic regulation” models, which our work suggests may operate together within neuroblast lineages through a combinatorial spatial transcription factor code — a model we refer to as the “distributed STF code model”.

      Strengths:

      The authors tested two models, "direct regulation" vs "epigenetic regulation" in a well-defined pool of neural stem cells during normal development.

      We thank the reviewer for recognizing this aspect of the study.

      Weaknesses:

      The data in this study cannot clearly substantiate these two models.

      Overall, there are a number of issues that are inconsistent and not supportive of the model proposed in this manuscript. Firstly, there is no evidence of pioneer factor activity in any of the NB lineages described - i.e., any changes in chromatin accessibility being shown over time. The authors must show chromatin conformation changes during the window of spatial transcription factor expression in order to convince the readers of this phenomenon.

      Thank you for raising this point. We agree that a developmental time course of chromatin accessibility would be very informative. The difficulty is that, in this system, the relevant sequence unfolds extremely rapidly and across two cellular contexts. Spatial transcription factors such as Gsb are expressed in the neuroectoderm; NBs then delaminate, and Hb expression begins almost immediately — on the order of minutes to tens of minutes. Before delamination there is no NB that can be targeted with lineage-specific drivers, whereas after NB formation the temporal program is already under way. This transition therefore cannot currently be resolved using lineage-restricted Dam profiling.

      In most studies, however, pioneer or chromatin-priming activity is inferred from a transcription factor’s ability to bind regions of relatively low accessibility and alter accessibility in a heterologous cellular context, rather than from direct developmental timecourse measurements. Our study provides both types of evidence: Gsb occupies accessible and relatively less-accessible regions in NB5-6 (Figure 4), while ectopic Gsb expression in NB7-4 causes bidirectional chromatin remodelling, with corresponding changes in Hb occupancy (Figure 6 and Figure 6—figure supplement 2).

      In light of this technical constraint, and in response to the reviewer’s broader concern, we have revisited our original interpretation. We now emphasise Gsb’s ability to alter chromatin accessibility bidirectionally and use the term “chromatin priming” rather than “pioneering.” We also make clear that Gsb does not act alone to establish the NB5-6 chromatin landscape, but functions within a broader, distributed STF code.

      Some examples of relevant manuscript changes are:

      - “We found, however, that Gsb’s role in chromatin remodelling was more complex: ectopic Gsb remodelled chromatin bidirectionally with regions both losing and gaining accessibility upon Gsb misexpression.”

      - “Thus, Gsb’s chromatin-remodelling capacity is not equivalent to autonomous opening of lineage-appropriate enhancers. Rather, these data support a model in which Gsb contributes to chromatin priming as part of a broader NB-specific STF code…”

      - “Thus, Gsb can engage loci and directly restrict chromatin accessibility at bound sites, but its ability to promote accessibility and Hb recruitment likely requires additional NB5-6-specific inputs.”

      Secondly, the phenotypic data do not align with the sequencing data - the story would be more cohesive if the sequencing data and phenotypic data were in the same NB subtypes. On one hand, we are shown that Gsb misexpression induces loss of chromatin accessibility in NB 7-4, however in the widespread loss model, we are not shown a phenotype in these NB7-4 - which suggest that the chromatin accessibility at these sites (sites that have already been distinguished as SoIs for that NB subtype) does not play an important role in distinguishing NB 7-4 identity. However, the authors report loss of NB3-5 identity but have no evidence as to how the chromatin has changed (or if it has at all) in that subtype, leaving the readers to wonder how the loss of identity occurred

      Thank you for raising this point regarding the alignment between the chromatin and phenotypic analyses. The reviewer’s comment made us realise that the rationale for these experiments may not have been sufficiently clear in the original manuscript and could therefore be perceived as misaligned. We therefore explain the logic of the experimental design here and have edited the manuscript to clarify this point for readers.

      The chromatin experiments were designed to test whether Gsb is capable of remodelling chromatin when introduced outside its endogenous domain. NB7-4 provided a convenient heterologous context with clean genetic access for TaDa/CATaDa profiling.

      Importantly, although NB7-4 was selected primarily for this technical reason, Gsb misexpression did affect NB7-4’s identity readout: at higher induction levels, the NB7-4-specific Gal4 reporter was lost. Because this same driver was required for lineage-restricted chromatin profiling, we titrated Gsb misexpression to a lower level that preserved reporter activity while still allowing us to assay chromatin accessibility and Hb occupancy. This calibration is documented in the new Figure 6—figure supplement 1. Thus, the calibrated profiling condition should not be interpreted as evidence that Gsb has no effect on NB7-4 identity; rather, it was necessary to retain genetic access to the lineage.

      The phenotypic experiments addressed a different question: the developmental requirement for Gsb within its endogenous spatial domain. Our analysis was therefore centred on NB5-6. Gsb depletion caused loss of the NB5-6-specific Lbe reporter and the NB5-6 neuronal markers Ap and Eya, whereas Gsb misexpression produced a partial gain of these markers (Figures 7 and 8). The accompanying changes in row-3/NB3-5 markers emerged as an additional reciprocal phenotype, consistent with Gsb established role in specifying row-5/6 identity while repressing row-3 fates, rather than as an independent endpoint that we had selected.

      Thus, the chromatin and phenotypic experiments are complementary: the NB7-4 experiment tests Gsb’s capacity to remodel chromatin in a convenient heterologous context, whereas the NB5-6 experiments establish its developmental function within its endogenous domain. We have revised the manuscript to make this distinction explicit.

      Relevant edits made to manuscript:

      “Having shown that ectopic Gsb remodels chromatin and alters Hb occupancy in the NB7-4 lineage, we next asked whether altering Gsb function changes NB lineage identity. The misexpression experiments described above had already shown that, when induced at high levels, Gsb misexpression turned off the NB7-4-lineage Gal4 reporter, consistent with Gsb suppressing NB7-4 identity at sufficient levels. In the chromatin-profiling experiments above, this was addressed by inducing lower Gsb misexpression levels that preserved Gal4 expression. Together, these observations suggested that Gsb misexpression can influence lineage identity as well as chromatin state. However, since NB7-4 is not an endogenous Gsb-expressing context, we turned to NB5-6 to test the developmental consequences of altering Gsb function.”

      Reviewer #2 (Public review):

      Summary:

      This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.

      We appreciate the reviewer’s careful summary of our findings and their clear articulation of the STF-code framework that emerges from the work.

      Strengths:

      The experiments are well-executed, the interpretations are generally sound, and the figures are clear and elegant. However, some conclusions are drawn too broadly without essential functional data. Therefore, additional work is needed to more effectively convey the central message.

      We thank the reviewer for their positive assessment of the experiments, interpretation, and figures, and we respond to their specific concerns below.

      Weaknesses:

      (1) Integration of TaDa and functional data on Gsb for the STF model

      The authors demonstrate that TaDa profiling maps Gsb binding across the genome and identifies candidate chromatin-priming sites in NB5-6. Gsb LOF/GOF experiments reveal effects on NB identity. Combining TaDa data with LOF and GOF analyses indicates that Gsb influences NB5-6 specification by binding to both open and relatively closed chromatin, helping maintain NB5-6 identity while limiting NB3-5 fate.

      However, the study does not establish a direct link between specific LOF/GOF phenotypes and particular genomic targets. For instance, analyzing Gsb occupancy at lineage-specific identity factors or terminal selector genes (such as Lbe, Ap, or Eya for NB5-6; and Ems, etc., for NB3-5) in wild-type and manipulated conditions (Gsb misexpression) would directly connect chromatin binding to the regulation of fate determinants. These investigations would strengthen the mechanistic connection between the correlative TaDa profiles and the observed identity changes, supporting the idea that Gsb functions as a context-dependent chromatin-priming factor within the STF code, rather than as a generic transcription factor.

      We thank the reviewer for this suggestion. We agree that examining Gsb occupancy at known lineage determinants helps connect the genome-wide TaDa profiles to the developmental phenotypes. We have therefore added browser profiles showing endogenous Gsb occupancy at several loci associated with NB5-6 identity and its neuronal output, including lbe, ap, and eya (new Figure 2E). These are the same markers that are lost following Gsb depletion and partially gained following Gsb misexpression (Figures 7 and 8), providing a locus-level connection between Gsb binding and the observed changes in NB5-6 identity.

      The precise comparison suggested by the reviewer is difficult because the binding and misexpression experiments were necessarily performed in different contexts. Gsb occupancy was measured in NB5-6, where Gsb is normally expressed, whereas the effect of Gsb misexpression on chromatin was measured in the heterologous NB7-4 context where Gsb is not normally expressed. We therefore did not measure Gsb occupancy following its misexpression in NB7-4 and so cannot compare Gsb binding before and after misexpression at individual loci in NB7-4.

      We have nevertheless added genome-browser examples showing how Gsb misexpression affects accessibility and Hb occupancy at lineage-associated loci (new Figure 6M). The ems locus provides one such example: ectopic Gsb reduces both accessibility and Hb occupancy at ems in NB7-4, while broad Gsb misexpression causes loss of Ems expression and of the independent NB3-5 reporter R59E09>GFP in the phenotypic experiments (Figure 8).

      (2) Gsb misexpression reveals bidirectional chromatin remodelling

      Experiments with ectopic Gsb expression demonstrate bidirectional chromatin remodelling in NB7-4, showing decreases in accessibility at some binding sites and increases at others. While the authors show that Gsb can disrupt chromatin upon misexpression, interpreting its "pioneer-like" or chromatin-priming activity is complex due to several factors: the misexpression occurs in a non-native lineage, the direct versus indirect effects rely on whole-embryo Dam-Gsb peaks instead of NB7-4-specific binding, and heat-shock-induced chromatin changes are not fully accounted for. These issues make it challenging to definitively determine Gsb's role in chromatin priming.

      A complementary approach would be to perform Gsb knockdown/loss-of-function in its native NB5-6 lineage and profile chromatin accessibility (TaDa or CATaDa). This would allow a cleaner, more physiologically relevant assessment of Gsb's contribution to priming, SoI establishment, and Hb recruitment. Such an experiment would strengthen the causal link between Gsb occupancy and chromatin state and clarify whether Gsb truly acts as a context-dependent pioneer in vivo, rather than producing indirect effects due to ectopic misexpression.

      We thank the reviewer for this thoughtful comment. The reviewer raises four related but distinct issues, which we address separately below.

      (1) Gsb was misexpressed in a non-native lineage

      As discussed in our response to Reviewer 1 regarding pioneer activity, expression in a heterologous cellular context is a standard experimental strategy for testing whether a transcription factor has the capacity to engage and remodel chromatin outside its endogenous setting. The use of NB7-4 was therefore deliberate: this experiment tested the sufficiency of Gsb to alter chromatin, rather than attempting to reproduce its physiological activity in NB5-6. Nevertheless, because Gsb produced bidirectional changes rather than autonomous chromatin opening, we have revised our interpretation. We now describe this experiment as demonstrating chromatin remodelling capacity and use “chromatin priming” rather than claiming a classical pioneer for its activity.

      Relevant edits made to the manuscript: Results, Figure 7 and Figure 7—figure supplements 1 and 2.

      (2) Direct and indirect effects were inferred using whole-embryo Gsb occupancy

      We have not profiled Gsb occupancy directly in NB7-4 following its misexpression. However, we have now performed an unbiased genome-wide analysis using two independent Gsb-binding datasets: stage-matched Gsb occupancy from NB5-6 and whole-embryo Gsb occupancy. Since Dam methylation is cumulative across the window of Gal4 activity, the whole-embryo dataset serves as a proxy for embryowide sites that are compatible with Gsb occupancy. Across both datasets, the result was strikingly consistent. The set of 1,340 loci that lost accessibility upon Gsb misexpression showed strong Gsb occupancy in both datasets, whereas the 606 loci that gained accessibility showed little or no Gsb occupancy in either dataset (Figure 6E,H). Thus, two independently generated Gsb-binding maps systematically distinguish the loci that close from those that open.

      This reproducibility — across many sites and two datasets — provides strong evidence that accessibility loss occurs at loci that Gsb can bind and is therefore likely to reflect a direct consequence of Gsb activity. Conversely, lower Gsb occupancy at sites that gain accessibility suggests that these gains are more likely to be indirect. The corresponding changes in Hb occupancy further strengthen this interpretation (Figure 6C–G).

      In summary, we find it particularly compelling that two independently generated Gsb-binding maps, obtained at different developmental stages and in different cellular contexts, identify the same systematic distinction: loci that close upon Gsb misexpression are Gsb-bound, whereas loci that open are not. We therefore consider this a strong and unbiased result supporting a direct role for Gsb in chromatin closing.

      Relevant edits made to the manuscript: (Results; Figure 6E, H)

      “Although we did not directly profile Gsb occupancy in NB7-4 upon misexpression, we asked whether the sites that lose or gain accessibility differ in their propensity to be bound by Gsb. To do this, we mapped independent Gsb occupancy datasets onto these two classes of remodelled sites: stage-matched Dam: Gsb from NB5-6, where Gsb is normally expressed, and whole-embryo Dam: Gsb, which provides a broader map of Gsb-compatible loci. In both datasets, sites that lost accessibility upon Gsb misexpression in NB7-4 showed strong Gsb occupancy, whereas sites that gained accessibility showed little or no Gsb signal. Thus, the most direct consequence of ectopic Gsb is likely accessibility loss at loci that Gsb can bind, and this loss is accompanied by reduced Hb occupancy. By contrast, accessibility gains occur mainly at loci with little evidence of Gsb binding in independent datasets, suggesting that these gains are more likely indirect consequences of Gsb misexpression.”

      (3) Heat-shock-induced chromatin changes were not fully accounted for

      We agree that, because heat shock is required to induce Gsb, a contribution from the induction regime cannot be completely excluded. However, several observations argue that the chromatin changes are not dominated by a non-specific heat-shock response. First, the accessibility changes were reproducible across biological replicates (Figure 6—figure supplement 2). Second, as described above, sites that lost accessibility were consistently occupied by Gsb in two independent datasets and showed a corresponding reduction in Hb occupancy, whereas sites that gained accessibility showed little or no Gsb occupancy (Figure 6C–H). This structured relationship would not be readily explained by a general heat-shock response.

      As an additional test, we performed a Gene Ontology analysis of genes associated with sites that gained accessibility. The enriched terms were predominantly related to development and cell-fate specification, including neuroblast differentiation and segment specification, while heat-shock and stress-response terms were not enriched (Author response image 1). Although none of these observations alone formally excludes an effect of heat shock, together they argue that the observed pattern reflects structured, Gsb-associated chromatin remodelling rather than a general stress response. We nevertheless interpret the accessibility gains cautiously as likely indirect effects of Gsb misexpression.

      Author response image 1.

      Gene ontology analysis of sites showing gain in accessibility upon Gsb misexpression in NB7-4. Genes overlapping the 606 sites showing at least 2-fold increase in accessibility (DamOnly signal) were annotated using the UCSC Table Browser (dm6). The resulting gene list was analysed for associated gene ontology (GO) terms using Metascape (Zhou et al. 2019). The top 20 enriched GO terms are represented as a bar chart, coloured by p-values.

      (4) Chromatin accessibility following Gsb loss in NB5-6 would provide a more physiological test

      We agree that this would be an informative experiment, and we explored several ways of perturbing Gsb early enough to address its endogenous function. A NB5-6-specific RNAi approach could not be used because the available NB5-6 Gal4 driver becomes active only after the period in which Gsb specifies the lineage.

      The difficulty with conventional gsb mutants is different but equally limiting. Homozygous embryos would have to be identified during the brief developmental window in which Hb is expressed, while each TaDa/CATaDa replicate requires at least 30 mg of precisely staged embryos. The available fluorescent balancer markers are either not detectable at these early stages or become reliably detectable only towards the end of the window. It is therefore not feasible to identify and collect sufficient homozygous embryos — either manually or by fluorescence-assisted whole-embryo sorting — within the required time frame.

      We therefore generated a gsb guide-RNA line and used Vasa-Cas9 to deplete Gsb in the early embryo. This strategy produced a high rate of Gsb editing, embryonic lethality and the expected axonal defects (Figure 7—figure supplement 1). When we examined NB5-6 in these embryos, we found that the Lbe-K reporter and the NB5-6 neuronal markers Ap and Eya were lost, confirming the requirement for Gsb in specifying this lineage (Figure 7).

      Unfortunately, this also meant that we could not take the experiment forward for TaDa/CATaDa. Lbe-K-Gal4 is the driver we use to target NB5-6, and its expression was lost following early Gsb depletion. Without the driver, we can no longer restrict Dam expression to NB5-6 and therefore cannot obtain a lineage-specific chromatin profile. Thus, although the early CRISPR strategy allowed us to examine the developmental consequences of Gsb loss, the same experiment could not be used to assay chromatin accessibility in NB5-6. We have made this limitation explicit in the revised Discussion.

      Relevant edits made to the manuscript: (Result, Figure 7 and Figure 7— figure supplement 1). We also now state explicitly in the “Scope and future directions” section that the current tools did not allow us to profile the chromatin consequences of Gsb loss directly in NB5-6 (Discussion).

      (3) En is not a pioneer factor

      The authors conclude that Engrailed (En) is not a pioneer factor, based on the observation that En binding correlates with accessible chromatin and that En is not enriched at NB5-6-specific SOIs. However, this conclusion is not sufficiently supported by the functional data.

      First, the absence of En binding at NB5-6-specific SOIs does not necessarily indicate an inability to engage closed chromatin. These regions were not selected for the presence of En consensus motifs, so their lack of occupancy may simply reflect the absence of En binding motifs rather than a lack of pioneering capacity. A systematic motif analysis at NB5-6-specific SOIs is needed to determine whether En binding sites are present but unoccupied.

      Second, the claim that En lacks pioneer activity relies solely on a single steady-state TaDa/DamID occupancy assay at one developmental stage. Because pioneer factor interactions can be transient, low-affinity, and stage-specific, such binding may not be detected by TaDa, which also depends on local GATC density and methylation kinetics and may yield false negatives. Given these technical limitations, the absence of En binding at less accessible regions does not definitively rule out a priming role.

      In the absence of direct functional assays (En LOF/GOF), the authors should explicitly acknowledge these technical and conceptual limitations and tone down the claim that "En lacks pioneer activity".

      We thank the reviewer for raising this point. We agree that the statement “En is not a pioneer factor” could be read as a general conclusion about En activity across developmental contexts. Our conclusion is more specific: in NB7-4 during the Hb-competence window, En occupancy is strongly biased towards pre-accessible chromatin and does not display the chromatin-engagement pattern expected of a pioneer factor. We have revised the manuscript throughout to make this context-specific interpretation clear. Below, we also address each of the reviewer’s points.

      (1) The absence of En at NB5-6-specific SoIs could reflect an absence of En-binding motifs

      We agree that the absence of En occupancy at NB5-6 SoIs would not be informative if these loci lacked En-binding motifs. We therefore performed the suggested motif analysis. En motifs were present at comparable frequencies in NB5-6- and NB7-4-specific SoIs (88.0% and 86.5%, respectively), with both enriched relative to matched shuffled controls (new Figure 3—figure supplement 1L). Thus, NB5-6 SoIs are motif-compatible but remain unoccupied by En in NB7-4. Their lack of En occupancy cannot therefore be explained simply by motif absence and instead points to additional determinants such as chromatin accessibility or cofactor context.

      Relevant edits made to the manuscript: Result, Figure 3B–C and new Figure 3—figure supplement 1L)

      “As expected, En was enriched at NB7-4-specific SoIs, where Hb uniquely binds in this lineage. However, there was no detectable En signal in NB7-4 at sites known to be NB5-6-specific SoIs, which remain relatively less accessible and Hb-free in NB7-4. This difference is unlikely to be explained simply by En motif availability, since En motifs are similarly represented at NB7-4- and NB5-6-specific SoIs. Thus, at SoIs, En occupancy is associated with the cognate, accessible NB7-4 SoIs, with little detectable occupancy at the non-cognate, relatively less-accessible NB5-6 SoIs.”

      (2) TaDa may fail to detect transient, low-affinity or stage-specific En binding

      We agree that no occupancy assay can exclude interactions below its detection threshold. However, TaDa is cumulative rather than a steady-state snapshot: Dam methylation accumulates during the expression window, allowing transient interactions to leave a detectable signal. Genome-wide, En occupancy was strongly weighted towards accessible chromatin, and accessibility predicted En binding with an AUROC of 0.74 (Figure 3E–I, Figure 3—figure supplement 2 and new Figure 3— figure supplement 3E).

      Gsb provides an internal comparison using the same experimental framework. Gsb binding was detected at both accessible and relatively less-accessible chromatin, and accessibility did not predict Gsb occupancy better than chance (AUROC = 0.41; Figure 4E–I and new Figure 3—figure supplement 3E). This argues that the En pattern is not caused by a general inability of TaDa to detect binding at less-accessible loci. We nevertheless acknowledge that rare interactions, interactions below the detection threshold or binding outside the assayed window cannot be excluded. The resolution and cumulative nature of TaDa are now also described explicitly in the revised manuscript.

      Relevant edits made to the manuscript: Result section for the genome-wide analysis and Discussion section for the TaDa limitations; Figure 3E–I, Figure 4E–I, Figure 3—figure supplement 2 and new Figure 3—figure supplement 3E).

      (3) Direct functional assays of En were not performed

      We agree that determining whether En actively establishes or maintains chromatin accessibility would require En loss- or gain-of-function experiments combined with chromatin profiling. These experiments were not performed in this study, and we have therefore removed the categorical statement that “En is not a pioneer factor.”

      The revised manuscript now states the more specific conclusion supported by our data: En occupancy in NB7-4 during the Hb-competence window is strongly biased towards pre-accessible chromatin. We also acknowledge that the current experiments cannot exclude rare or transient interactions below the detection threshold, or chromatin-remodelling activity by En at other loci, developmental stages or cellular contexts. The factors that establish the NB7-4 accessibility landscape therefore remain unresolved.

      Relevant edits made to the manuscript: (Result; Figure 3)

      “Taken together, these results show that En occupancy is strongly biased toward preaccessible chromatin in NB7-4, including at NB7-4-specific SoIs. Thus, En is well placed to integrate spatial and temporal information through direct regulation at preaccessible enhancers. However, because NB7-4 has a distinct accessibility landscape, the factors that establish this landscape remain unresolved. We therefore propose that NB7-4 may use a combinatorial STF-code logic, in which other lineage-specific factors help establish NB7-4-specific SoIs, while En occupies this pre-accessible landscape and likely collaborates with Hb to activate lineage-specific enhancers.”

      (4) Clarity of STF-code Model and Central Message

      The manuscript begins by presenting two models, direct and epigenetic, but the central takeaway of the paper is not clear. Specifically, the nuanced roles of the spatial factors Gsb and En as chromatin-priming versus stabilizing/effector factors within an STF code, and the resulting division of labor, are not clearly illustrated. The distinction between Gsb as a chromatin-priming factor and En as a cofactor-dependent activator/stabilizer should be explicitly presented in a stepwise model for better clarity. The authors could strengthen this by providing a schematic with two sequential stages illustrating how neuroblast identity factors (STF code) change chromatin states to drive lineage-specific enhancer activation. The schematic can be shown from the neuroectoderm to individual NB lineages to make it more panoramic.

      We thank the reviewer for this suggestion. This comment, together with related points raised below, made us realise that the manuscript did not bring out strongly enough that our data lead us to propose a third model: the distributed STF-code model. Rather than treating direct and epigenetic regulation as mutually exclusive alternatives, this model distributes these activities across different members of a NB-specific STF code. We have now revised the manuscript to reflect this transition. For example, we now state this explicitly in the Abstract, at the end of the Introduction and at the beginning of the Discussion.

      Relevant edits made to the manuscript: Abstract, Introduction, Discussion, and have added a new summary schematic (new Figure 9).

      (5) Identification of Priming Factors in NB7-4

      While the authors suggest that an unknown priming factor might be responsible for establishing sites of integration in NB7-4, they do not identify or explore potential candidates for this role. Further investigation into what factors might be involved in chromatin priming in NB7-4 could provide a more complete understanding of the mechanisms at play.

      We thank the reviewer for this suggestion. We agree that identifying the factors that establish the NB7-4 accessibility landscape is an important next step. Our current data do not identify such a factor, and doing so would require systematic perturbation of candidate STF-code members together with lineage-specific chromatin profiling.

      We have nevertheless made this question more concrete in the revised Discussion. We identify Msh as a candidate shared input in NB5-6 and NB7-4, highlight additional candidates suggested by motif enrichment at NB7-4 SoIs, and propose NB6-4 — which coexpresses Gsb and En — as a useful context in which to test the division of labour between STF-code members. We present these as testable candidates rather than established priming factors.

      (6) Functional Validation of STF Code Components

      The study proposes an STF code for each neuroblast lineage, but the specific components of these codes, beyond Gsb and En, are not fully explored. Identifying and validating additional factors that contribute to the STF code in each lineage could strengthen the conclusions.

      We thank the reviewer for this point. We agree that the complete composition of the NB5-6 and NB7-4 STF codes remains to be established, and we now make this explicit. The present study proposes a mechanistic framework based on the different chromatin-engagement properties of Gsb and En, rather than claiming to have defined or validated every component of either code.

      As noted in our response above, the revised Discussion identifies Msh, Iroquois-complex members and TF families enriched at NB7-4 SoIs as candidate additional components. Testing these factors individually and in combination, with chromatin, Hb-occupancy and lineage-identity readouts, will be required to define the minimal functional code for each lineage. We present this as an important direction for future work (lines 759–770).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The reviewers appreciate the content shown in Figure 1, which is helpful for people who are unfamiliar with the system as a whole. One key issue is that later in the manuscript, the authors look at Gsb and describe it as a factor that works to close chromatin, making the model depicted in panel E misleading when reading the first half of the manuscript and leading to confusion/whiplash when getting to Figure 6, where a spatial transcription factor is thought to play a role in closing chromatin.

      Thank you for pointing this out. Figure 1 was intended to present the two models currently proposed in the field and as the starting hypotheses for the study. It was not intended to present the conclusions from our study. We realise, however that this distinction was not clear and since we did not present the updated model, it made the later finding appear contradictory.

      We have now made substantial edits to the text and have made the transition from the original models to our revised interpretation explicit in the Introduction and Discussion. We have also added a new summary model (new Figure 9), which shows that chromatin opening, chromatin closing and STF–Hb engagement can be distributed across different members of an NB-specific STF code.

      (2) It is unclear what "less accessible" actually means. The reviewers would like to see quantitative standardization to define the degree of chromatin accessibility when using the Dam expression alone.

      We thank the reviewer for this point. The comment made us revisit how we defined accessibility, which we had previously treated too much as a binary distinction between accessible and inaccessible chromatin. We have now revised our analyses to represent chromatin accessibility as a continuum of states.

      We defined a common set of 27,119 DamOnly-marked regions across both NBs, ranked them by mean DamOnly signal and divided them into deciles from least to most accessible. The construction and validation of this continuum are shown in new Figure 2G and new Figure 2—figure supplement 2A–D. We also use this framework to examine STF occupancy and Gsb-dependent chromatin remodelling across the accessibility spectrum (new Figure 3— figure supplement 2A and new Figure 6—figure supplement 2E–F).

      Where a discrete lineage-specific comparison was required, we applied a minimum two-fold difference in DamOnly signal between the NBs. This identified 845 NB5-6-preferential and 1,067 NB7-4-preferential regions. A region preferentially accessible in one NB is therefore described as relatively less accessible in the other, rather than as completely inaccessible.

      Relevant edits made to the manuscript: Results, new Figure 2G and new Figure 2—figure supplement 2A–D.

      (3) A key takeaway in Figure 3 is that En is binding already accessible chromatin. Another key point that this manuscript makes is that spatial-temporal factors (and codes) are responsible for this opening. We have not yet been shown that chromatin accessibility of this neuroblast identity actually changes upon spatial-temporal factor expression. I suggest including data of the dam-only control as a time course. Showing chromatin accessibility just before temporal factor expression and then after to validate that pioneer factor activity appears to be likely.

      We appreciate this suggestion and agree that, conceptually, the model implies a temporal sequence in which spatial factors first act during neuroectoderm patterning and temporal factors such as Hb subsequently engage the chromatin after neuroblast formation. In practice, however, these events occur in very rapid succession during development. Spatial transcription factors are expressed in the neuroectoderm, neuroblasts are specified and delaminate, and Hb expression begins almost immediately after neuroblast formation — all within a short developmental window on the order of minutes to tens of minutes.

      This makes it difficult to directly test chromatin accessibility changes across these stages using lineage-restricted Dam-based approaches. Before delamination there is no neuroblast to target with the available drivers, and once the neuroblast forms the temporal program is already underway. As a result, the sequential changes in accessibility that the reviewer is suggesting cannot currently be resolved with the available tools.

      We agree that directly visualizing how chromatin accessibility changes during this brief developmental transition would be very interesting, but doing so would likely require approaches with much higher temporal resolution, such as live imaging-based methods for monitoring chromatin accessibility.

      (4) Similar to the comment for Figure 3, showing chromatin accessibility changes in the NB subtype via a time course that includes both before and after spatial transcription factor expression begins.

      This point is closely related to the reviewer’s previous comment regarding a temporal analysis of chromatin accessibility. Please refer to our response above describing the developmental timing and technical limitations that make such an experiment difficult in this system.

      (5) It is difficult to reconcile the conclusion in Figure 5 that regions bound by both En/Hb or Gsb/Hb are considered the highest accessibility. Can the authors touch on the logic for this, given that in the next figure, they are going to suggest that Gsb works as a factor that is primarily responsible for closing chromatin? It is unclear how Gsb is closing chromatin, but it is also binding Hb, and in those regions, chromatin is most accessible.

      We thank the reviewer for raising this point. The findings in Figures 5 and 6 may initially appear difficult to reconcile, but we think that they both actually point to the same conclusion: that Gsb’s effect on chromatin is context-dependent.

      An important point is that Gsb binding is not restricted to a single chromatin state. In its endogenous NB5-6 context, it occupies both accessible and relatively less-accessible regions (Figure 4B–C, E–F). Its presence at accessible chromatin does not establish that Gsb opened that chromatin nor that it closed it because these occupancy data do not reveal the direction of chromatin change. From these data we can only conclude that sites co-bound by Gsb and Hb are more accessible than sites bound by Gsb alone, suggesting that the outcome of Gsb binding depends on the surrounding regulatory context. In the presence of Hb — and likely other members of the NB5-6 STF code — Gsb can participate in a highly accessible, productive regulatory state (Figure 5C–E).

      The misexpression experiment helps us interpret these relationships in the endogenous context. When Gsb is introduced into NB7-4, sites that lose accessibility are strongly Gsb-bound in two independent datasets, and this loss is accompanied by reduced Hb occupancy (Figure 6C–H). Some Gsb-bound sites also gain accessibility, consistent with bidirectional remodelling, but Gsb occupancy is negligible at these gain sites. Thus, outside its normal NB5-6 regulatory environment, the more direct effect of Gsb binding is likely chromatin closing.

      This provides a possible explanation for the endogenous (NB5-6) data in Figure 5. The lower accessibility of sites bound by Gsb alone may reflect this closure-biased activity, whereas the highly accessible state at Gsb–Hb co-bound sites is likely to arise from the broader NB5-6 regulatory context. We do not conclude that every Gsb-only site is actively closed, or that Gsb itself opens the co-bound sites. Rather, our data suggest that Gsb can participate in different chromatin states, with the outcome of its binding shaped by Hb and other members of the STF code.

      We have revised the Results and Discussion to make this interpretation clearer.

      (6) The most striking piece of data from Figure 6 is that it appears the physiological role of Gsb is to reduce chromatin accessibility and that opening chromatin is an indirect effect. I am not sure that the field widely recognizes closing as chromatin as "pioneer-like" activity. Moreover, this finding seemed very out of place given the way the story has been sold thus far - discussing "priming of chromatin" and referring to this as opening in figure 1 and discussion of binding of these factors at open and less accessible.

      Thank you for raising this issue, which overlaps with the reviewer’s previous point. We were equally struck by the result from Figure 6: the sites that lose accessibility are more consistent with a direct effect of Gsb, whereas the sites that gain accessibility are more likely to represent indirect effects. This suggests that Gsb can remodel chromatin bidirectionally.

      We agree that the field does not generally describe chromatin closing as pioneer activity. We therefore no longer refer to Gsb as a pioneer factor. Instead, we describe Gsb as having bidirectional chromatin-remodelling capacity and refer to it as “chromatin priming” activity.

      Finally, we agree that the original presentation made this progression difficult to follow. Figure 1 presents the two existing models as the starting point, whereas our data lead us to propose a third, distributed STF-code model. We now make this transition explicit throughout the revised manuscript and have added a new summary schematic (new Figure 9; See Results and Discussion).

      Reviewer #2 (Recommendations for the authors):

      Minor comment:

      (1) Have the authors validated the specificity of the Lbe-GAL4 and 19B03AD;18F07DBD drivers? It is important to clarify whether these drivers label only NB5-6 and NB7-4 neuroblasts or also their progeny. Since the interpretation of chromatin dynamics is in the context of neuroblasts, reads from differentiated neurons could affect the conclusions.

      We thank the reviewer for raising this point. These driver lines were extensively characterised in our previous study and are highly specific to the NB5-6 and NB7-4 lineages, respectively. Importantly, as the reviewer points out, they are not neuroblast-exclusive and also label progeny. Because our profiling is restricted to the early Hb competence window, this contribution should be limited to a small number of GMCs and the earliest-born neurons.

      On rereading the manuscript, we realised that there were several instances where we used “NB-specific” too loosely when referring to these datasets. We have therefore revised the manuscript throughout to make clear that the TaDa/CATaDa profiles represent the NB5-6 and NB7-4 lineages during the Hb window and are not exclusively derived from the neuroblast itself. We have also added this explicitly as a limitation in the Scope and Future Directions section. Importantly, this does not change the essence of our conclusions, which are based on comparisons between these two defined lineages and on perturbations examined within the same lineage-restricted framework.

      Relevant edits made to the manuscript:

      - “The driver lines were previously validated as being specific to these lineages”.

      - “First, the lineage-specific Gal4 drivers also label the immediate progeny of each NB; thus, although profiling was restricted to the early Hb competence window, when only a small number of GMCs and early-born neurons are present, the resulting profiles are not exclusively derived from the NB itself.”

      (2) The lineage NB6-4 co-expresses both Gsb and En and represents an informative intermediate case. This context supports a cooperative STF-code model in which Gsb provides chromatin priming while En acts as an effector or stabilizer, without requiring autonomous pioneer activity. The authors should briefly discuss how NB6-4 fits into their model and whether Gsb priming in this lineage enables En-Hb co-occupancy and enhancer activation.

      We thank the reviewer for this comment. We agree that NB6-4 is an especially interesting case in the context of our model, because it co-expresses both Gsb and En and therefore may provide a lineage in which both aspects of the proposed two-step mechanism can be examined together: Gsb could contribute to chromatin priming and En could function at already primed enhancers together with Hb. We now include this explicit example in the Discussion section.

      (3) The authors' data support a role for Gsb in lineage-specific chromatin priming, but the functional experiments reveal important limitations. Gsb alone does not efficiently open closed chromatin, install NB5-6-specific SOIs, or recruit Hb, and ectopic Gsb often antagonizes native chromatin states. Thus, the results do not support Gsb as a classical standalone pioneer factor, as implied in the discussion. The authors should replace the term "pioneer-like potential or activity" with "context-dependent pioneer activity" in the results, as they have rightly mentioned in the discussion.

      We agree with the reviewer. As discussed in our responses to the related comments above, our data do not support describing Gsb as a classical pioneer factor. Gsb remodels chromatin bidirectionally, with both gains and losses of accessibility, whereas pioneer activity is traditionally associated with chromatin opening. We have therefore gone further than the reviewer’s suggested change: throughout the manuscript, we have replaced “pioneer-like potential/activity” and “context-dependent pioneer activity” with “context-dependent chromatin remodelling” or “chromatin priming.” The Results and Discussion now make it clear that Gsb’s activity depends on the surrounding STF and cofactor context rather than representing autonomous pioneer activity.

      (4) The use of ectopic expression systems may introduce artifacts or off-target effects that could affect the interpretation of the results. These potential limitations are not extensively discussed.

      We agree that ectopic expression can produce indirect or non-physiological effects. The Gsb misexpression experiment was designed to test whether Gsb is sufficient to remodel chromatin in the heterologous NB7-4 context, rather than to reproduce its endogenous function in NB5-6. We therefore do not interpret every change in accessibility in the misexpression experiment as a direct or physiological role for Gsb in NB5-6. However, the changes are not random. Sites that lose accessibility are strongly associated with Gsb occupancy in two independent datasets and they also lose Hb occupancy, whereas sites that gain accessibility show little Gsb occupancy and are therefore more likely to reflect indirect effects (Figure 6C–H). The changes were reproducible between replicates, and genes associated with accessibility gains were not enriched for heat-shock or stress-response terms (Author response image 1). These analyses do not remove the limitations of ectopic expression, but they help distinguish the more likely direct effects from indirect ones. We now make both the purpose and the limitations of this experiment explicit in the Results and Discussion.

      (5) The moderate correlation (0.7-0.82) between Dam-En and Dam-only reflects the expected overlap of En binding with accessible chromatin, but it also underscores a limitation: TFs that prefer open regions may show high baseline similarity to Dam-only, potentially masking low-affinity or transient interactions. The authors should acknowledge this when concluding that En lacks pioneer activity.

      We thank the reviewer for bringing this up. We want to clarify that our conclusion that En preferentially occupies accessible chromatin is not based only on the moderate genome-wide correlation between Dam: En and DamOnly. We also examined En binding at defined SoIs and at differentially accessible loci across the genome. In both analyses, En is strongly enriched at accessible NB7-4 sites but shows little detectable enrichment at the corresponding less-accessible sites (Figure 3B–F). We have now extended this analysis across 27,119 regions ranked along the full accessibility spectrum. Here too, En signal remains low across the less-accessible bins and increases principally in the most accessible bins (new Figure 3— figure supplement 2E).

      A useful comparison here is Gsb. Using the same experimental and analytical framework, we readily detect Gsb binding at both accessible and relatively less-accessible chromatin in NB56 (Figure 4B–F; new Figure 3—figure supplement 2E). Thus, the method is able to detect binding at less-accessible chromatin when it occurs. Consistent with this, accessibility predicts En occupancy but not Gsb occupancy in the corresponding ROC analysis (new Figure 3—figure supplement 3E). We also refer the reviewer to our response to the related comment above, where we discuss this comparison in more detail.

      Finally, we want to point out that TaDa is not a snapshot assay. Dam-mediated methylation accumulates over the expression window and across many cells, so repeated weak or transient interactions should, in principle, contribute to the signal. We agree, however, that very rare interactions below the detection threshold cannot be excluded. We have therefore tempered our conclusion throughout the manuscript: rather than stating that En lacks pioneer activity, we now conclude that En occupancy during the NB7-4 Hb window is strongly biased towards pre-accessible chromatin.

      (6) The schematic illustrating the NB 5-6 and NB 7-4 SOIs (Figures 3A, 4A, and S2A) should be made clearer. When Hb is bound, sites that are accessible serve as SOIs for a specific lineage, whereas Hb-unbound, inaccessible sites do not. The authors can visually represent this by showing that, in the case of Hb binding, the accessible region functions as a resident SOI for the NB7-4 lineage, while the same region in NB5-6, being Hb-free, is inaccessible. This distinction could be indicated with a shaded or shadowed area from top to bottom in Figures 3A and 4A.

      We thank the reviewer for pointing this out. We have revised these panels to make the visual distinction between lineage-specific SoIs and the corresponding Hb-free, less-accessible loci in the other lineage more explicit.

      (7) At 472, change "Fig.S6C-F" to "Fig.S6C-D".

      Thank you for spotting that!

    1. But Little Red Cap took care and went straight to grandmother's. She told her that she had seen the wolf, and that he had wished her a good day, but had stared at her in a wicked manner. "If we hadn't been on a public road, he would have eaten me up," she said.

      This is the part where Little Red Cap learns her lesson not to trust dangerous strangers, and goes the right course of action.

    1. Moral: Children, especially attractive, well bred young ladies, should never talk to strangers, f

      This is a bit weird, but also knowing that his audience was aristocrats, it makes sense.

  2. social-media-ethics-automation.github.io social-media-ethics-automation.github.io
    1. Chain letter. December 2023. Page Version ID: 1188532303. URL: https://en.wikipedia.org/w/index.php?title=Chain_letter&oldid=1188532303 (visited on 2023-12-08).

      This link introduces chain letters, which are messages that ask whoever receive them to pass to others. It talks about how chain letters spread through paper mail before becoming common in email, social media posts, and so on. Some even use scary stories to share them.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements [optional]

      We thank to all Reviewers for their careful consideration of our manuscript. We highly appreciate their thoughtful comments and suggestions that helped us improve the quality of our work. We address each comment point-by-point below.

      2. Point-by-point description of the revisions

      Reviewer #1:

      Major comments

      1. It is rather difficult to conclude whether the observed nipple phenotype reflects an early embryonic/prepubertal defect in establishing the nipple stroma, is caused by a constitutive response to ongoing cell death, or a response to continuous DTA expression (or a combination of some of these). The data raise a couple of additional questions: Is there a nipple phenotype at 3 wk of age? It would not be totally unsurprising if ablation of a major fraction of dermal fibroblasts in the nipple area would lead to an early embryonic/prepubertal phenotype but there is no data on this. Hence, is there a "congenital" nipple deformity, as concluded by the authors (line 191)?

      We appreciate the reviewer’s insightful comments. We have now included data on embryonic nipple development. These data demonstrate abundant S100A4-lineage cells in E15.5 and E18.5 skin of S100a4-Cre;mT/mG embryos (Figure for Reviewers 1a, corresponding to Figure S4a in the revised manuscript) and normal appearance of nipple sheath in S100a4-Cre;DTA embryos at E18.5 (Figure for Reviewers 1b, corresponding to Figure S4b in the revised manuscript), suggesting no embryonic defect.

      Unfortunately, we cannot provide data on 3-weeks old mice (we have not collected this timepoint previously and currently we do not have this mouse line alive). Instead, however, we provide in situ pictures of DTA and S100a4-Cre;DTA nipples at 7 weeks of age (Figure for Reviewers 1c; Figure S4c in the revised manuscript), which demonstrate that the phenotype of defective nipple is fully established at this timepoint. Because the late embryonic data did not support the “congenital” establishment of the nipple deformity and we could not provide any more data from early postnatal development, we have corrected the statement “we describe a congenital nipple deformity” in the discussion (line 202) to “depletion…results in nipple deformity”.

      Are there S100a4+ cells in the nipple area of pubertal S100a4-Cre/DTA mice? I.e. is there a continuous supply of new S100a4+ cells and thereby continuous cell death and DTA expression as one might expect based on the RNA-seq data?

      The S100A4+ cells are present in the nipple area of S100a4-Cre;DTA mice, suggesting a continuous supply of new S100A4+ cells (Figure for Reviewers 1b, corresponding to Figure S4b in the revised manuscript; and Figure for Reviewers 5a,b, corresponding to Figure S8a,b in the revised manuscript). In the revised manuscript, we comment on this in Discussion (lines 263–268): “Notably, we observed incomplete depletion of S100A4+ cells in the mammary gland and nipple. Interestingly, the same S100a4-Cre;DTA mouse model resulted in complete depletion of S100A4+ cells in the superficial layer of the mandibular condyle46. This suggests that incomplete depletion of S100A4+ cells in the nipple and mammary gland is due to tissue-specific dynamics, rather than lack of depletion efficiency, indicating a compensatory mechanism that can balance the cell loss.”

      The subtitle on line 54 implies that that S100a4-Cre/DTA mice display a branching phenotype. However, it looks to me as if there is a pubertal outgrowth defect (as is also written in the body text, line 64) rather than a branching phenotype, potentially reflecting the much smaller size of S100a4-Cre/DTA mice (Fig. 2a). Unless there is a change in branch point frequency, I suggest rephrasing the title and discussion. Instead, I suggest the authors discuss the observed outgrowth delay considering the gross overall growth defect (Fig. 2a). If ductal outgrowth was normalized to the overall growth defect, would one still observe 'a delay in branching morphogenesis'?

      We apologize for the section title confusion. We have analyzed branching frequency in 7-weeks-old females and observed reduced total number of branching points in S100a4-Cre;DTA mice (Figure for Reviewers 2a, corresponding to Figure 2f in the revised manuscript). A significant difference in number of branching points remained also after their normalization to body weight, (Figure for Reviewers 2c, corresponding to Figure 2h in the revised manuscript). We have now added the new quantifications to the revised manuscript with accompanying descriptions in the main text (lines 63-68) “Analysis of mammary epithelial development using whole-mount carmine staining revealed no significant differences in the prenatal establishment of the mammary epithelial tree but it did reveal significantly delayed epithelial outgrowth and reduced branching in pubertal (7 weeks old) S100a4-Cre;DTA mice (Figure 2d-f). Normalization of epithelial outgrowth and branching to body weight indicates that the observed defect represents a mammary-specific impairment rather than a consequence of reduced body growth (Figure 2g,h).”

      Fig. 4e shows Masson's Trichrome and Picrosirius Red staining and the authors report the findings as follows (lines 120-124): "collagen fibers were loosened in the DTA nipples and more densely packed in the S100a4-Cre;DTA nipples". Perhaps the authors could help non-specialists to observe the loosened fibers and if they wish to make quantitative statements ("more densely packed"), such statements should be backed-up by quantifications.

      Picrosirius red staining viewed under polarized light is a classic way to assess collagen organization, thickness, and packing. Red/orange/yellow color typically marks thicker, more mature, and more tightly packed collagen fibers (often associated with type I collagen), while green color usually marks thinner, less organized, or less densely packed fibers (often associated with type III collagen or immature collagen). We had included this explanation in the Figure 4 legend of the submitted manuscript already: “Typically, thicker collagen fibers exhibit stronger birefringence and appear red or orange, while thinner fibers exhibit weaker birefringence and appear green or yellow.” To help with the quantification, we have extracted the red channel and quantified color intensity. The results are shown in Figure for Reviewers 3a,b, corresponding to Figure S5 in the revised manuscript. To investigate the morphology of collagen fibers in more detail, we performed collagen fiber analysis CT-FIRE1 (Figure for Reviewers 3c). The analysis suggests that the fibers in S100a4-Cre;DTA nipples might be longer and less straight (Figure for Reviewers 3d,e); however, the result is statistically nonsignificant.

      I found the Discussion on the various mouse models somewhat problematic. Overall, the paper is written is a way that it often remains unclear whether it refers to studies addressing the role of S100a4 itself, studies addressing the function of S100a4+ cells via ablation approaches (S100a4-Cre or S10 0a4-CreERT2 crossed with floxed DTA), or those where S100a4-Cre has been used to delete gene X/Y/Z. These are all very different experimental approaches where one approach is not necessarily informative when trying to understand the results from another one. The authors should make these points clear and consider whether all their discussion points are relevant.

      We apologize for the confusion. We have carefully reviewed the references and their interpretations and corrected them as necessary.

      The abstract states S100a4 (fibroblast-specific protein 1) is "expressed by mesenchymal cells and has been implicated in the development of eccrine glands, hair follicles, and mammary branching morphogenesis". However, the study on eccrine glands (ref. 19) shows that S100A4+ cells play a role in eccrine gland development but it does not address the role of S100a4 itself, while the study on hair follicles (ref.20) in turn reports the expression pattern of S100a4 in hair follicles but does not address its function, nor the role of S100a4+ cells. Finally, I failed to find references in the paper to studies addressing the role of S100a4, or S100a4+ cells in the mammary gland.

      Instead, the paper had references to studies where S100A4-Cre had been used to delete different genes and these mice had various mammary phenotypes - which, as indicated above, is a very different approach compared to deleting S100a4 or ablating S100a4+ cells.

      Thank you for your comment. We have addressed the issues in the Abstract and in the Discussion. We revisited the cited studies more carefully, clearly distinguishing the different approaches and particular findings. In our literature review, we also considered studies that used S100a4-Cre mouse model to manipulate gene expression within S100A4+ cells. We argue that these studies bring indirect evidence of involvement of S100A4-expressing cells (and the particular targeted gene in them) in development and/or homeostasis of a tissue, such as mammary gland. Please, find the rephrased part of Abstract in the text, and below:

      “S100A4 (S100 calcium binding protein A4, also known as fibroblast-specific protein 1) is expressed by mesenchymal cells and has been associated with hair follicle regeneration. S100A4-expressing cells have been implicated in the development of eccrine glands, and studies using S100a4-Cre to manipulate gene function have suggested that S100A4-expressing cells may contribute to mammary branching morphogenesis.”

      __In Discussion (lines 197-200), __the authors write: "We described significant delay in mammary branching morphogenesis in puberty, confirming an important role for S100A4+ cells in mammary development, as it was previously described (refs 37-39)."

      It should be noted that none of these studies addressed the role of S100A4+ cells:

      • Ref 37 used S100a4-Cre to delete sharpin

      • Ref 38 used the same Cre line to delete Ptch1, did not address the role of S100a4 or S100a4 expressing cells

      • Likewise ref 39 deleted another gene using S100a4-Cre

      Later on in Discussion, the authors compare the reported phenotype to previous studies (lines 248-255): "...targeting S100A4+ cells through knockout experiments can result in severe phenotypes, such as a reduction in adipose tissue (ref 26), skin phenotypes, a disrupted estrous cycle, reduced fertility (ref. 38), and complete infertility, hypogonadism and defects in pituitary endocrine function (ref. 28).

      Of these, Ref. 26 used the same approach as the current study (S100a4-Cre; DTA) (Fig. 7A in the paper)

      • these mice were significantly lean, with markedly reduced fat compared with the control mice - also the mice in the current study are very small, so perhaps they could also be described as 'lean'. Yet ref. 26 reports that female mice had comparable food uptake, respiratory exchange ratio and physical activity, and slightly increased energy expenditure

      Ref. 38 (as mentioned above) reports deletion of Ptch1 using S100a4-Cre lines and these mice "displayed a disrupted estrous cycle and dramatically reduced fertility over 6.5 weeks". However, this has nothing to do with the approaches where Fsp1/S100a4+ cells are depleted with DTA. Likewise, reference 28 analyzed the phenotype of S00a4-Cre;Ptch1fl/fl mice. Obviously, deleting Ptch1 using S100a4-Cre mice is quite a different approach than "targeting S100A4+ cells" through knockout experiments". Ptch1 deletion leads to a combination of gain-of-function (of Hedgehog activation) and loss-of-function (loss of Hh-independent functions of Ptch1) and hence comparisons with these phenotypes is rather challenging. I suggest the authors focus their phenotype comparisons to ref. 26 where S100a4/Fsp1+ cells were ablated with DTA, i.e. the same approach as in the current study.

      Please find the rephrased part of Discussion in the text (lines 242-262), and below:

      “A key consideration when interpreting studies involving S100A4 is that fundamentally different experimental approaches have been used to investigate its role. These include descriptive analyses of S100A4 expression, functional studies targeting the S100A4 protein itself, genetic models using S100a4-Cre to manipulate unrelated genes in S100A4-expressing cells, and ablation models such as S100a4-Cre;DTA, which deplete S100A4⁺ cells. These approaches are not equivalent and provide distinct types of information. In the present study, we specifically assess the consequences of ablating S100A4-expressing cells, and comparisons to other studies should therefore be interpreted within this context.

      Studies using S100a4-Cre to manipulate specific signaling pathways (e.g. Wnt or Hedgehog signaling via gene deletion) in S100A4-expressing cells have reported diverse phenotypes, including effects on fertility and endocrine function28,44. However, these phenotypes primarily reflect the consequences of pathway perturbations within S100A4-expressing cells rather than the role of S100A4⁺ cells themselves. This is fundamentally different from the ablation approach used here, which removes the S100A4⁺ cell population.

      In contrast, studies employing S100a4-Cre–driven DTA–mediated ablation represent a directly comparable approach. Such studies have reported systemic phenotypes, including reduced adipose tissue and altered metabolic parameters26, indicating that S100A4-expressing cells contribute to multiple aspects of tissue homeostasis. Consistent with these previous reports, S100a4-Cre;DTA mice used in our study were significantly smaller than their littermates. Our findings extend these observations by identifying a specific and previously unrecognized role for this cell population in nipple morphogenesis.”

      I find the Discussion is somewhat off the topic by starting with WHO recommendations on breastfeeding and linking this to observed mouse phenotype. Overall, the discussion is rather long and from time-to-time more like a literature review. I would recommend keeping the Discussion more succinct and focused.

      To improve the conciseness and focus of Discussion, we have deleted this part of text.

      Minor comments:

      Figure 5 would be more informative if it included more higher magnification images that would reveal the staining at the cellular level.

      To fulfil the suggestion, we have performed a new round of immunostaining followed by high-resolution confocal imaging. We have now updated Figure 5 in the revised manuscript.

      **Referee cross-comenting**

      I agree with the comments of other reviewers. However, to me it seems that the analysis of S100a4 knockout mice would not be feasible within a reasonable timeframe and would represent a study of its own. My understanding was that the authors were not interested in S100a4 itself. Rather, S100a4-Cre was used as a tool to understand the importance of a certain (fibroblast) cell population for mammary gland morphogenesis.

      Indeed, our goal was to study the role of a specific cell population (S100A4+ cells) in mammary gland morphogenesis, not to study the role of S100A4 protein per se.

      Reviewer #1 (Significance (Required)):

      General assessment:

      This study reveals the importance of the S100a4+ cell lineage for nipple formation while showing the same cells are dispensable for mammary gland morphogenesis. The main limitation is that it remains unclear whether the observed nipple phenotype is derived from an early embryonic/prepubertal defect in establishing the nipple stroma, is caused by a constitutive response to ongoing cell death, or a response to continuous DTA expression (or a combination of some of these). Hence its relevance as a model of human inverted nipple condition remains rather speculative.

      Thank you for consideration of our work and for your valuable feedback. We did not intend to claim that S100a4-Cre;DTA mouse represents a model of human inverted nipple condition. However, considering morphological features, it does resemble it. We now rephrased the Discussion so it is clearer and more concise.

      Reviewer #2

      Major comments:

      1. My key concern is the discussion part. I think the authors need to re-organize/re-phrase the discussion part, it confused me a bit in terms of logic, phrases and interpretation of literatures.

      We have significantly re-organized and re-phrased the Discussion.

      Here are few examples:

      1. The lines 195-199 contain lot of repeated information

      We have rephrased the paragraph and removed repeated information. The new text can be found in lines 200-205 in the revised manuscript.

      1. The authors mentioned the studies in ref 26,28 and 38 using "targeting S100A4+ cells through knockout experiment can result in sever phenotypes". This is very misleading. Those studies using the same (or similar if the origin is different) S100A4-Cre line as the current study but induced the activation of Wnt and sHH signalling pathways, respectively. The observed phenotypes are largely due to the pathway function, rather than the S100A4 gene or normal S100A4+ cell itself. This is significantly differed from the current study.

      We apologize for the confusion; we have now rephrased our claims (lines 242-262):

      “A key consideration when interpreting studies involving S100A4 is that fundamentally different experimental approaches have been used to investigate its role. These include descriptive analyses of S100A4 expression, functional studies targeting the S100A4 protein itself, genetic models using S100a4-Cre to manipulate unrelated genes in S100A4-expressing cells, and ablation models such as S100a4-Cre;DTA, which deplete S100A4⁺ cells. These approaches are not equivalent and provide distinct types of information. In the present study, we specifically assess the consequences of ablating S100A4-expressing cells, and comparisons to other studies should therefore be interpreted within this context.

      Studies using S100a4-Cre to manipulate specific signaling pathways (e.g. Wnt or Hedgehog signaling via gene deletion) in S100A4-expressing cells have reported diverse phenotypes, including effects on fertility and endocrine function28,44. However, these phenotypes primarily reflect the consequences of pathway perturbations within S100A4-expressing cells rather than the role of S100A4⁺ cells themselves. This is fundamentally different from the ablation approach used here, which removes the S100A4⁺ cell population.

      In contrast, studies employing S100a4-Cre–driven DTA–mediated ablation represent a directly comparable approach. Such studies have reported systemic phenotypes, including reduced adipose tissue and altered metabolic parameters26, indicating that S100A4-expressing cells contribute to multiple aspects of tissue homeostasis. Consistent with these previous reports, S100a4-Cre;DTA mice used in our study were significantly smaller than their littermates. Our findings extend these observations by identifying a specific and previously unrecognized role for this cell population in nipple morphogenesis.”

      1. In the lines 253-255, why the author believe complete S100A4+ depletion would leads to the fatal of mouse? Is there study suggest that? Or have authors checked the expression of S100A4 in the S100A4-Cre;DTA model to confirm the efficiency?

      We have revised our claim. We have now included, also in response to other Reviewers’ comments, data on S100A4 expression in the S100A4-Cre;DTA model (Figure for Reviewers 5, corresponding to Figure S8 in the revised manuscript), and commented on these results in lines 263-268: “Notably, we observed incomplete depletion of S100A4+ cells in the mammary gland and nipple. Interestingly, the same S100a4-Cre;DTA mouse model resulted in complete depletion of S100A4+ cells in the superficial layer of the mandibular condyle46. This suggests that incomplete depletion of S100A4+ cells in the nipple and mammary gland is due to tissue-specific dynamics, rather than lack of depletion efficiency, indicating a compensatory mechanism that can balance the cell loss.”

      The authors tried to attribute the minor phenotype to the incomplete depletion of S100A4+ cells. However, it is possible that if the S100A4+ cells only represented a minor population, their function may be compensated by other populations. This might be confirmed by quantification of S100A4+ cells or S100A4-Cre; GFP+ cells in fibroblast or CD45 populations from images showed in Figure 5.

      We have now added quantification of GFP+ lineage cell’s contribution to immune cell and fibroblasts populations (Figure 5 in the revised manuscript). Moreover, we have now included data on the presence of S100A4+ cells in S100a4-Cre;DTA mice (Figure for Reviewers 5a,b; Figure S8a,b in the revised manuscript), which demonstrate incomplete depletion of the S100A4+ cells in the nipple and the mammary gland. This is likely due to ongoing tissue remodeling and continuous S100A4+ replenishment/ supply. Another study using the same S100a4-Cre;DTA mouse model reported an efficient S100A4+ cell depletion in mandibular condyle2, which suggests that the presence of S100A4+ cells in the S100a4-Cre;DTA mammary gland and nipple is due to tissue-specific dynamics rather than lack of depletion efficiency.

      We have included in Discussion: “Notably, we observed incomplete depletion of S100A4+ cells in the mammary gland and nipple. Interestingly, the same S100a4-Cre;DTA mouse model resulted in complete depletion of S100A4+ cells in the superficial layer of the mandibular condyle46. This suggests that incomplete depletion of S100A4+ cells in the nipple and mammary gland is due to tissue-specific dynamics, rather than lack of depletion efficiency, indicating a compensatory mechanism that can balance the cell loss.”

      In Fig. 1, the authors described the impaired nursing capacity of S100A4-Cre;DTA dam. However, it seems the little size is also smaller (Fig 1a). Do authors have any explanation or hypothesis?

      Thank you for this insightful observation. It is well established that metabolic and nutritional condition directly affect female reproductive functions. Adult S100A4-Cre;DTA mice are generally smaller compared to their litter counterparts, potentially because of lower body fat content or other anatomic/metabolic condition that might negatively influence fecundity, for instance, lowering ovulation rate and/or embryonic survival. In support of this, earlier studies have reported a positive correlation between growth rate/body condition and litter size3. Unfortunately, in the case of S100A4-Cre;DTA mice, we can only speculate about the possible explanations, as we do not have supporting data which could confirm it.

      The images in Figure 5 and Figure S4 are difficult to confirm colocalization. A higher magnification image would be required for each panel. Furthermore, a precise quantification based on the current images would be more supportive of the conclusion regarding the discrepancy of the composition of S100A4 lineage between epidermis and mammary gland (lines 163-165).

      To address this comment, we performed a new round of immunostaining and high-resolution confocal imaging and quantifications (Figure 5 and Figure S7 in the revised manuscript). In the nipple, GFP+ cells were found among both immune cells and fibroblasts, with prevalent expression by fibroblasts (Figure 5e). In the mammary gland, the S100a4-lineage cells comprised mostly immune cells and only very few GFP+ fibroblasts (Figure S7).

      Line 163, the author hypothesis the Langerhans cells due to morphology. Those cells should be able to be confirmed by a co-staining with F4/80 in addition to the current form of Fig 5h.

      We performed a co-staining of GFP and AIF1, a macrophage marker expressed by Langerhans cells, and found GFP+ AIF1+ cells localized in the epidermis (Figure 5f in the revised manuscript). This result confirmed that the cells are Langerhans cells.

      In lines 181-184, the authors states "the results showed that the tissue reacted to a foreign chemical or an endogenous compound....." , which results are referring here? I could not find any inflammation related GO terms in figure 6b. It would be more accurate to specify them in lines 179-181, which appears to be a technical statement rather than a result in current form.

      Thank you for this comment. Indeed, there are no GO terms explicitly labeled as “inflammation” and “repair”; however, several GO terms are functionally related to these processes. Our interpretation was based on broader biological context rather the explicit annotation. To clarify this, we revisited the text and included GO terms that reflect the tissue response (lines 192-196).

      “The GO terms indicated that the tissue reacted to a foreign chemical or an endogenous compound (xenobiotic metabolic process, cellular response to xenobiotic stimulus, response to xenobiotic stimulus, epoxygenase P450 pathway), and responded to inflammation and repair (actin filament-based process, actin cytoskeleton organization; eicosanoid and lipid metabolic processes) (Figure 6b).”

      The lines 182-184 was not clear. Does the author refer the "nipple tissue response" in general as malfunction of development or inflammation and tissue repair as mentioned in the previous sentence? If the later cases, the authors should consider the failure of lactation might mimic the involution, which may cause the apoptosis and inflammation as well. This might be independent of the DTA expression.

      Thank you for raising this point. Indeed, in this line, we refer to ongoing tissue inflammation and repair. We also considered the hypothesis that ejection incapability (and consecutive milk stasis) triggers involution. However, tissues were collected within a few hours after parturition, when only very early signs of involution, if any, would be detectable; therefore, we expect minimal influence of involution. To reflect this comment, we added new text to the Discussion (lines 278- 283). “The observed tissue response can be also associated with hallmarks of mammary involution, the process which is triggered by the milk stasis. However, the tissues were collected within few hours after parturition, when the effect of involution should be minimal51. Rather, we hypothesize that immune cell recruitment, and the upregulation of the lipid skin barrier might be caused in response to the continuous apoptosis of S100A4+ cells and their replacement.”

      Minor comments:

      1. The authors demonstrated in Figure S1 and lines 92-96 that no significant differences were observed in pituitary glands and ovaries in S100a4-Cre:DTA and DTA mice. Have the authors checked the S100A4 expression or lineage cells in these organs, or have been reported by others?

      Yes, we had checked the S100A4-lineage cells in the pituitary gland and ovary and have now included the results in the manuscript (Figure for Reviewers 4a,b corresponding to Figure S2a,b in the revised manuscript), along with relevant text description (lines 97-98 in the revised manuscript). “We observed S100A4-lineage/traced cells in the pituitary gland and ovaries of S100a4-Cre;mT/mG mice (Figure S2a,b).” The presence of S100A4+ cells in these organs was also reported previously5.

      The authors have performed live imaging to evaluate the contraction of alveoli. It would be better to include a video together with the snapshots showed in Figure S2.

      We have included the videos as Supplementary movies, Movie S1 (DTA) and Movie S2 (S100a-Cre;DTA).

      Since the study is mainly using S100a4, it would be better to avoid using FSP1 in the results, for example Fig 5h.

      We apologize for this oversight; it has now been corrected.

      What does L1 stand for? Lactation Day 1? It should be spelt out in the first instance.

      Yes, indeed, L1 is lactation day 1. Please note that it was already spelled out in the first version of the manuscript, now in line 48.

      Line 150. Figure S4 should be Figure S4a.

      (Please note that by adding new Supplementary figures, this comment is referring to Figure S7 in the new version of manuscript.) Thank you for this comment. In the text (lines 155-156), we state “GFP+ cells were spread throughout the fat pad but were also localized in the periepithelial stroma and infiltrated the epithelium”. This we show in Figure S7a and in S7b; therefore, we now changed the reference accordingly, as it might be more accurate.

      **Referee cross-comenting**

      I agree with the other reviewers, as well as the Consultation Comments. The manuscript would benefit greatly from a thoroughly optimised Discussion section to address issues raised by all reviewers.

      __ Reviewer #2__ (Significance (Required)):

      • Overall, this study is well designed and the key findings are valid, especially the role of S100A4 during nipple development is novel and interesting.

      -One limitation of the study is that RNA-seq was performed using a mixture of all cell types present in the nipple. While this approach is reasonable-given that depletion of the S100A4+ lineage may exert both direct and indirect effects contributing to nipple dysfunction-it should be more clearly acknowledged and discussed in the manuscript. Additionally, this experimental design may limit the utility of the dataset for other researchers interested in nipple development and the specific functions of S100A4.


      Reviewer #3

      Major comments:

      2) The differential systemic versus mammary-specific effects of DTA-mediated S100A4 cell ablation are intriguing. The authors should address why the mammary fat pad appears unaffected.

      Thank you for this comment. The role of S100A4+ cells in adipose tissue was previously reported6. Authors reported significantly lower weight of fat pads/ inguinal white adipose tissue (I-WAT) in S100a4-Cre;DTA mice (both males and females), as well as smaller size of I-WATs and smaller adipocytes in I-WATs from S100a4-Cre;DTA mice. In our work, we measured the area of the fat pad, and adipocyte size in distal fat pad and found no significant differences between the DTA and S100a4-Cre;DTA mice. The discrepancy in absence vs. presence of the altered adiposity phenotype between our study and the published study6 might be due to different background of the experimental mice. Our mice were on C57BL/6 background, while the mice in the published study were backcrossed to FVB/N background. There is quite strong evidence that mouse genetic background affects adiposity, adipose-tissue expansion, and where/how excess lipid is stored, and there are several direct comparisons of FVB/N vs. C57BL/6 (PMID: 22964790; PMID: 19325873; PMID: 23423668; PMID: 28483861; PMID: 28605665 etc.).

      Are S100A4 expressing cells present during embryonic mammary development, or are they mainly postnatal? Would an inducible S100A4CreERT model lead to similar phenotypes, or might the timing of depletion influence the outcome? Discussing these points would reinforce the conclusions regarding the contribution of S100A4-expressing cells to mammary and nipple development and could also clarify the transient nature of the ductal branching phenotype.

      S100A4-expressing cells are present during embryonic mammary development, too. Please note the embryonic lineage-tracing time-points incorporated in the first version of the manuscript already (Figure 5a and Figure S7a). Now, we have added Figure for Reviewers 1, corresponding to Figure S4 in the revised manuscript, which focuses on the embryonic nipple phenotype but also provides information on the presence of S100A4+ cells.

      On top, we examined available data from previously published work7–10 to investigate S100a4 expression in early and late development of the mammary gland and the nipple (Figure for Reviewers 5). S100a4 was detected in the epithelium and fibroblasts in early postnatal (P0) mammary gland. At later stages (pubertal mammary gland development), its expression was shifted predominantly to leukocytes, with reduced levels in fibroblasts. In contrast, scRNA seq data from embryonic skin revealed broad expression of S100a4 within the fibroblast cluster, along with a small, distinct immune cells population expressing S100a4. In adult skin, S100a4 expression was observed in epidermal cells, immune cells, and fibroblasts.

      We agree that the use of inducible S100a4-CreERT model could potentially bring new insights into developmental stage-specific roles of S100A4+ cells. However, such experiments are beyond our capacity. Therefore, we have included a new subsection on Limitations of the study, where we comment (lines 292-296):

      “A limitation of this study is that the timing of DTA-mediated cell depletion cannot be precisely defined in the constitutive mouse model employing S100a4-Cre because recombination may occur continuously following the initial expression of S100a4 (E8.518). This limitation could be overcome by usage of inducible S100a4-CreERT instead, which could help determine if the nipple phenotype is established embryonically or during postnatal morphogenesis.”

      3) Although the authors attribute lactation failure primarily to defects in nipple architecture, the RNA seq data reveal downregulation of key milk production genes and luminal differentiation keratins, strongly suggesting impaired secretory activation. The authors should more explicitly discuss the relative contributions of epithelial functional maturation defects versus nipple structural abnormalities to the lactation failure observed upon S100A4+ cell depletion.

      Thank you for this suggestion. We agree that secretory activation might be impaired in S100a4-Cre;DTA mice; more specifically the suckling/milk removal-dependent maintenance and amplification of mammary secretory function and postpartum alveolar remodeling (Wagner et al., 1997, PMID: 9678900). In this regard, we have included new text in Discussion (lines 283-286):

      “The transcriptomic signature also revealed downregulation of genes essential for milk production and luminal epithelial differentiation. This potentially reflects the lack of suckling/milk removal-dependent post-partum alveolar maturation and maintenance of secretory activation52.” To a small extent, the results may be also influenced by differences in tissue composition between samples. In control mice, more fully developed nipples and an extended ductal network likely contribute to a greater representation of differentiated luminal epithelial cells, thereby increasing the abundance of expression of these markers.

      Minor comments:

      1. Figure 1: Including an immunohistochemistry or immunofluorescence control confirming depletion of S100A4 expressing cells would strengthen the conclusions.

      We have now included Figure for Reviewers 6 that corresponds to Figure S8 in the revised manuscript and comment on the results in sections Results (lines 175-176) and Discussion (lines 263-268). In Results: “Interestingly, S100A4 antibody labeling revealed presence of S100A4+ cells in S100a4-Cre;DTA tissues (Figure S4b, Figure S8a,b).” In Discussion: “Notably, we observed incomplete depletion of S100A4+ cells in the mammary gland and nipple. Interestingly, the same S100a4-Cre;DTA mouse model resulted in complete depletion of S100A4+ cells in the superficial layer of the mandibular condyle46. This suggests that incomplete depletion of S100A4+ cells in the nipple and mammary gland is due to tissue-specific dynamics, rather than lack of depletion efficiency, indicating a compensatory mechanism that can balance the cell loss.”

      Figure 2c: The H&E images are not fully convincing. Immunofluorescence analysis of epithelial architecture would support the authors' interpretation and should be feasible if tissues are already available.

      We have now included Figure for Reviewers 7 that corresponds to Figure 2d in the revised manuscript. We performed immunostaining for keratin 5 (KRT5, basal – myoepithelial cells), keratin 8 (KRT8, luminal cells) and α smooth muscle actin (αSMA, basal – myoepithelial cells) on adult (16 weeks of age) mammary gland sections. The epithelial architecture in S100a4-Cre;DTA was preserved, with no detectable abnormalities. Accordingly, we comment on this in the Results section (lines 61-62): “Immunofluorescent examination of epithelial organization also showed no detectable abnormalities in S100a4-Cre;DTA mammary glands.”

      Figure 3c: The histological defects more accurately reflect failure of secretory activation rather than "lactation failure" per se. The terminology should be refined to reflect this more precisely.

      Thank you for this comment. As explained in the response to your major comment 3, we believe our results show that the secretory activation is conserved in S100a4-Cre;DTA lactating mice. We understand that “lactation failure” might be misleading terminology, as the production of the milk is conserved as well. We therefore change the phrasing into “nursing defect” (line 51, 75, 85, 210), as this could reflect the phenotype most precisely.

      Figure 4f: The proliferation data are compelling, but the authors could extend this by examining how cell differentiation and epithelial organisation are affected.

      We have now included Figure for Reviewers 8 that corresponds to Figure S6 in revised manuscript. We performed immunostaining for keratin 5 (KRT5), keratin 8 (KRT8), keratin 10 (KRT10) and α smooth muscle actin (αSMA) on lactating nipple sections. The epithelial organization is well preserved in S100a4-Cre;DTA nipples.

      We comment on this in Results section (lines 133-135): “There were no abnormalities in epithelial organization, smooth muscle or sebaceous glands along the lactiferous duct in the S100a4-Cre;DTA L1 nipple (Figure S6a-c).”

      Figure 5b: To more convincingly show that GFP+ cells contact endothelial cells, co-labelling with an endothelial marker such as CD31 would be helpful.

      We performed the requested co-labeling of GFP and CD31 (Figure for Reviewers 9 and Figure 5c in the revised manuscript). Our result confirms the contact between GFP+ cells and endothelial cells, as GFP+ cells were present within the vasculature.

      Figure 5f-h: The structures referenced in the text (lines 159-163) should be clearly indicated on the immunofluorescence images.

      We incorporated these explanations into the new, high-resolution/detailed Figure 5 in the revised manuscript.

      **Referee cross-comenting**

      I agree with the Reviewer, the authors do not need to do knockout experiments in the revised manuscript. However, it would be great if they could address my comment in the discussion.

      Reviewer #3 (Significance (Required)):

      This is an important study for mammary developmental biology, addressing the relatively understudied mechanisms that govern nipple development at the stromal-epithelial interface, and the determinants of lactational performance. A major strength is the elegant integration of DTA-mediated cell ablation, advanced imaging, lineage tracing, and transcriptomics to uncover previously uncharacterised roles for S100A4-expressing stromal populations in shaping nipple morphology and function. The work lays a foundation for future studies into nipple biology and pathologies and mechanisms underlying successful lactation.

      Although the study is already mature, it could be further strengthened by incorporating more specific genetic models, such as inducible S100A4CreERT or S100A4 gene knockout/knockdown approaches.

      Thank you for appreciation of our work.

      1. Description of analyses that authors prefer not to carry out

      Reviewer #1

      Major Comment 1.

      It is rather difficult to conclude whether the observed nipple phenotype reflects an early embryonic/prepubertal defect in establishing the nipple stroma, is caused by a constitutive response to ongoing cell death, or a response to continuous DTA expression (or a combination of some of these). The data raise a couple of additional questions: Is there a nipple phenotype at 3 wk of age?...

      Unfortunately, we cannot provide data on 3 weeks old mice because we did not collect such samples earlier and we had to terminate our mouse colony due to an infection in the animal facility (mouse line reanimation is possible because we had stored sperm of the mouse line but it would take too much time and resources). Nevertheless, we tried to address this comment by providing other relevant available data (see Figure for Reviewers 1).

      Reviewer #2

      Major Comment 3.

      In Fig S1c, d and lines 93-96, the authors investigated the estrus cycles to determine the potential cause of lactation failure. The data was presented as the number of mice in each stage. A more intuitive approach would be to follow the same mice for two to three cycles and observe the duration of each stage.

      We agree that the suggested approach would be more accurate in determining truly cycling females. Unfortunately, we cannot perform this experiment currently because we do not have this mouse line alive anymore. Nevertheless, because the S100a4-Cre;DTA females bore pups, they had cycled and were fertile.

      Reviewer #3

      Major comment 1.

      While the S100A4Cre::DTA model is powerful for evaluating the roles of S100A4 expressing cells, the authors should discuss the potential outcomes of using S100A4 knockout or knockdown approaches. If the authors have such data available, this could help distinguish phenotypes caused by loss of S100A4 function itself from those arising due to ablation of S100A4 expressing cell populations and would add mechanistic depth to the study.

      We thank the Reviewer for this insightful suggestion. We agree that genetic approaches targeting S100A4 function (e.g., knockout or knockdown) could, in principle, help disentangle cell-autonomous effects of S100A4 from those resulting from the loss of S100A4-expressing cell populations. However, the primary objective of our study was to investigate the functional contribution of S100A4+ stromal cells at the population level, rather than to dissect the molecular function of S100A4. In this context, the S100A4-Cre;DTA model provides a well-established and appropriate strategy to ablate this cell population and assess its role in tissue development. Importantly, S100A4 is not only a functional protein but also a widely used marker of a heterogeneous stromal cell population. Genetic ablation of S100a4 itself would not eliminate these cells, and may result in relatively subtle or compensable phenotypes due to functional redundancy within the S100 protein family or context-dependent roles of S100A4. Therefore, such approaches would address a distinct biological question and may not directly recapitulate the phenotypes observed upon cell ablation.

      References

      1. Bredfeldt, J.S., Liu, Y., Pehlke, C.A., Conklin, M.W., Szulczewski, J.M., Inman, D.R., Keely, P.J., Nowak, R.D., Mackie, T.R., and Eliceiri, K.W. (2014). Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer. J. Biomed. Opt. 19, 016007. https://doi.org/10.1117/1.JBO.19.1.016007.
      2. Tuwatnawanit, T., Wessman, W., Belisova, D., Sumbalova Koledova, Z., Tucker, A.S., and Anthwal, N. (2025). FSP1/S100A4-Expressing Stem/Progenitor Cells Are Essential for Temporomandibular Joint Growth and Homeostasis. J. Dent. Res. 104, 551–560. https://doi.org/10.1177/00220345251313795.
      3. Eisen, E.J., and Durrant, B.S. (1980). Genetic and Maternal Environmental Factors Influencing Litter Size and Reproductive Efficiency in Mice. J. Anim. Sci. 50, 428–441. https://doi.org/10.2527/jas1980.503428x.
      4. Friedman, G., Levi-Galibov, O., David, E., Bornstein, C., Giladi, A., Dadiani, M., Mayo, A., Halperin, C., Pevsner-Fischer, M., Lavon, H., et al. (2020). Cancer-associated fibroblast compositions change with breast cancer progression linking the ratio of S100A4+ and PDPN+ CAFs to clinical outcome. Nat. Cancer 1, 692–708. https://doi.org/10.1038/s43018-020-0082-y.
      5. Ren, Y.A., Monkkonen, T., Lewis, M.T., Bernard, D.J., Christian, H.C., Jorgez, C.J., Moore, J.A., Landua, J.D., Chin, H.M., Chen, W., et al. (2019). S100a4-Cre–mediated deletion of Ptch1 causes hypogonadotropic hypogonadism: Role of pituitary hematopoietic cells in endocrine regulation. JCI Insight 4. https://doi.org/10.1172/jci.insight.126325.
      6. Zhang, R., Gao, Y., Zhao, X., Gao, M., Wu, Y., Han, Y., Qiao, Y., Luo, Z., Yang, L., Chen, J., et al. (2018). FSP1-positive fibroblasts are adipogenic niche and regulate adipose homeostasis. PLoS Biol. 16. https://doi.org/10.1371/journal.pbio.2001493.
      7. Carabaña, C., Sun, W., Veludo Ramos, C., Huyghe, M., Perkins, M., Maillot, A., Journot, R., Hartani, F., Faraldo, M.M., Lloyd-Lewis, B., et al. (2024). Spatially distinct epithelial and mesenchymal cell subsets along progressive lineage restriction in the branching embryonic mammary gland. EMBO Journal 43, 2308–2336. https://doi.org/10.1038/s44318-024-00115-3.
      8. Sumbal, J., Journot, R.P., Attri, K., Danek, V., Merle, C., Faraldo, M.M., Sumbalova Koledova, Z., and Fre, S. (2025). Contractile fibroblasts form a transient niche for the branching mammary epithelium. Nature Communications 16. https://doi.org/10.1038/s41467-025-63612-x.
      9. Jacob, T., Annusver, K., Czarnewski, P., Dalessandri, T., Kalk, C., Levra Levron, C., Campamà Sanz, N., Kastriti, M.E., Mikkola, M.L., Rendl, M., et al. (2023). Molecular and spatial landmarks of early mouse skin development. Dev. Cell 58, 2140-2162.e5. https://doi.org/10.1016/j.devcel.2023.07.015.
      10. Joost, S., Annusver, K., Jacob, T., Sun, X., Dalessandri, T., Sivan, U., Sequeira, I., Sandberg, R., and Kasper, M. (2020). The Molecular Anatomy of Mouse Skin during Hair Growth and Rest. Cell Stem Cell 26, 441-457.e7. https://doi.org/10.1016/j.stem.2020.01.012.
      11. Wagner, K., Young, W., Liu, X., Ginns, E., Li, M., Furth, P., and Hennighausen, L. (1997). Oxytocin and milk removal are required for post‐partum mammary‐gland development. Genes Funct. 1, 233–244. https://doi.org/10.1046/j.1365-4624.1997.00024.x.
    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this pre-print, Belisova et al. investigate the under-explored mechanisms regulating nipple development and its essential role in offspring nourishment, focusing on the contribution of S100A4‑expressing cells in the mouse mammary gland. The authors use an elegant combination of Cre::DTA-mediated cell depletion, lineage tracing, imaging, RNA-seq, and functional assays to reveal roles for S100A4‑expressing fibroblasts and immune cells in nipple morphogenesis and lactation. The manuscript is generally well written, and the experimental design is strong, with appropriate controls supporting the overall conclusions. However, I have several comments and suggestions to improve this initial manuscript.

      Major comments:

      1) While the S100A4Cre::DTA model is powerful for evaluating the roles of S100A4 expressing cells, the authors should discuss the potential outcomes of using S100A4 knockout or knockdown approaches. If the authors have such data available, this could help distinguish phenotypes caused by loss of S100A4 function itself from those arising due to ablation of S100A4 expressing cell populations and would add mechanistic depth to the study.

      2) The differential systemic versus mammary-specific effects of DTA-mediated S100A4 cell ablation are intriguing. The authors should address why the mammary fat pad appears unaffected. Are S100A4 expressing cells present during embryonic mammary development, or are they mainly postnatal? Would an inducible S100A4CreERT model lead to similar phenotypes, or might the timing of depletion influence the outcome? Discussing these points would reinforce the conclusions regarding the contribution of S100A4-expressing cells to mammary and nipple development and could also clarify the transient nature of the ductal branching phenotype.

      3) Although the authors attribute lactation failure primarily to defects in nipple architecture, the RNA seq data reveal downregulation of key milk production genes and luminal differentiation keratins, strongly suggesting impaired secretory activation. The authors should more explicitly discuss the relative contributions of epithelial functional maturation defects versus nipple structural abnormalities to the lactation failure observed upon S100A4+ cell depletion.

      Minor comments:

      1. Figure 1: Including an immunohistochemistry or immunofluorescence control confirming depletion of S100A4 expressing cells would strengthen the conclusions.

      2. Figure 2c: The H&E images are not fully convincing. Immunofluorescence analysis of epithelial architecture would support the authors' interpretation and should be feasible if tissues are already available.

      3. Figure 3c: The histological defects more accurately reflect failure of secretory activation rather than "lactation failure" per se. The terminology should be refined to reflect this more precisely.

      4. Figure 4f: The proliferation data are compelling, but the authors could extend this by examining how cell differentiation and epithelial organisation are affected.

      5. Figure 5b: To more convincingly show that GFP+ cells contact endothelial cells, co-labelling with an endothelial marker such as CD31 would be helpful.

      6. Figure 5f-h: The structures referenced in the text (lines 159-163) should be clearly indicated on the immunofluorescence images.

      Referee cross-comenting

      I agree with the Reviewer, the authors do not need to do knockout experiments in the revised manuscript. However, it would be great if they could address my comment in the discussion.

      Significance

      This is an important study for mammary developmental biology, addressing the relatively understudied mechanisms that govern nipple development at the stromal-epithelial interface, and the determinants of lactational performance. A major strength is the elegant integration of DTA-mediated cell ablation, advanced imaging, lineage tracing, and transcriptomics to uncover previously uncharacterised roles for S100A4-expressing stromal populations in shaping nipple morphology and function. The work lays a foundation for future studies into nipple biology and pathologies and mechanisms underlying successful lactation.

      Although the study is already mature, it could be further strengthened by incorporating more specific genetic models, such as inducible S100A4CreERT or S100A4 gene knockout/knockdown approaches.

      I have expertise in mammary epithelial biology.

      I estimate that revisions would require 3-6 months if new experiments are performed, and 1-3 months if revisions focus on clarifying claims and strengthening the discussion.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, Jaros Belisova et al. systematically investigated the composition and roles of S100A4+ cells during mammary gland development and identified a novel role for S100A4 for nipple development. Depletion of S100A4+ lineage using the S100a1-Cre;DTA model resulted in delayed pubertal mammary gland development but with normal morphology and milk production capacity during lactation. The authors further investigated the milk-ejection function of the alveoli using an ex vivo culture method combined with live imaging. This revealed that depletion of S100A4+ cells does not interfere with the normal function of alveoli. However, the abnormal development of the nipple, characterised by smaller size, shorter length, lacking protrusion, increased collagen composition and decreased cell proliferation at the onset of lactation, results in milk delivery failure which is responsible for the lethality of the pups. To further understand the consequences of S100A4+ cell depletion, the author utilised the S100a4-Cre;mTmG model to trace the cell types depleted in the DTA model across various developmental stages. Immunofluorescent staining revealed that S100A4 lineage cells comprised both fibroblasts and immune cells, consistent with previous studies. Interestingly, some S100A4 lineage (GFP+) retain the expression of S100A4. In addition, the RNAseq data comparing the nipple cells from S100a4-Cre;DTA and DTA lactation mice confirmed their observations in transcription level. Overall, the experiments are well designed and the key findings are valid, especially about the role of S100A4 during nipple development is novel and interesting.

      Major comments:

      1. My key concern is the discussion part. I think the authors need to re-organize/re-phrase the discussion part, it confused me a bit in terms of logic, phrases and interpretation of literatures. Here are few examples:

      a. The lines 195-199 contain lot of repeated information

      b. The authors mentioned the studies in ref 26,28 and 38 using "targeting S100A4+ cells through knockout experiment can result in sever phenotypes". This is very misleading. Those studies using the same (or similar if the origin is different) S100A4-Cre line as the current study but induced the activation of Wnt and sHH signalling pathways, respectively. The observed phenotypes are largely due to the pathway function, rather than the S100A4 gene or normal S100A4+ cell itself. This is significantly differed from the current study.

      c. In the lines 253-255, why the author believe complete S100A4+ depletion would leads to the fatal of mouse? Is there study suggest that? Or have authors checked the expression of S100A4 in the S100A4-Cre;DTA model to confirm the efficiency?

      d. The authors tried to attribute the minor phenotype to the incomplete depletion of S100A4+ cells. However, it is possible that if the S100A4+ cells only represented a minor population, their function may be compensated by other populations. This might be confirmed by quantification of S100A4+ cells or S100A4-Cre; GFP+ cells in fibroblast or CD45 populations from images showed in Figure 5. 2. In Fig. 1, the authors described the impaired nursing capacity of S100A4-Cre;DTA dam. However, it seems the little size is also smaller (Fig 1a). Do authors have any explanation or hypothesis? 3. In Fig S1c, d and lines 93-96, the authors investigated the estrus cycles to determine the potential cause of lactation failure. The data was presented as the number of mice in each stage. A more intuitive approach would be to follow the same mice for two to three cycles and observe the duration of each stage. 4. The images in Figure 5 and Figure S4 are difficult to confirm colocalization. A higher magnification image would be required for each panel. Furthermore, a precise quantification based on the current images would be more supportive of the conclusion regarding the discrepancy of the composition of S100A4 lineage between epidermis and mammary gland (lines 163-165). 5. Line 163, the author hypothesis the Langerhans cells due to morphology. Those cells should be able to be confirmed by a co-staining with F4/80 in addition to the current form of Fig 5h. 6. In lines 181-184, the authors states "the results showed that the tissue reacted to a foreign chemical or an endogenous compound....." , which results are referring here? I could not find any inflammation related GO terms in figure 6b. It would be more accurate to specify them in lines 179-181, which appears to be a technical statement rather than a result in current form. 7. The lines 182-184 was not clear. Does the author refer the "nipple tissue response" in general as malfunction of development or inflammation and tissue repair as mentioned in the previous sentence? If the later cases, the authors should consider the failure of lactation might mimic the involution, which may cause the apoptosis and inflammation as well. This might be independent of the DTA expression.

      Minor comments:

      1. The authors demonstrated in Figure S1 and lines 92-96 that no significant differences were observed in pituitary glands and ovaries in S100a4-Cre:DTA and DTA mice. Have the authors checked the S100A4 expression or lineage cells in these organs, or have been reported by others?
      2. The authors have performed live imaging to evaluate the contraction of alveoli. It would be better to include a video together with the snapshots showed in Figure S2.
      3. Since the study is mainly using S100a4, it would be better to avoid using FSP1 in the results, for example Fig 5h.
      4. What does L1 stand for? Lactation Day 1? It should be spelt out in the first instance.
      5. Line 150. Figure S4 should be Figure S4a.

      Referee cross-comenting

      I agree with the other reviewers, as well as the Consultation Comments. The manuscript would benefit greatly from a thoroughly optimised Discussion section to address issues raised by all reviewers.

      Significance

      • Overall, this study is well designed and the key findings are valid, especially the role of S100A4 during nipple development is novel and interesting.
      • One limitation of the study is that RNA-seq was performed using a mixture of all cell types present in the nipple. While this approach is reasonable-given that depletion of the S100A4+ lineage may exert both direct and indirect effects contributing to nipple dysfunction-it should be more clearly acknowledged and discussed in the manuscript. Additionally, this experimental design may limit the utility of the dataset for other researchers interested in nipple development and the specific functions of S100A4.

      My expertise:

      mammary gland development and breast cancer

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      In this study, Belisova et al. investigate the function of S100a4+ (a.k.a. Fsp1) cells in the mammary gland. S100a4 expressing cells were constitutively ablated using the DTA system by crossing S100a4-Cre mice with ROSA26-eGFP-DTA mice. Female mice exhibited a severe nursing defect, leading to whole-litter mortality within 1-2 days postpartum. However, no abnormalities were detected in the morphology of the mammary ductal tree, milk production, or alveolar contractility of S100a4-Cre;DTA mice. Instead, nipples were malformed, likely prevent normal suckling. Analysis of the lineage of S100a4 expressing cells in the mammary gland using the S100a4-Cre mouse in combination with a fluorescent Cre reporter identified S100a4+ cells as fibroblasts and immune cells in the nipple region, while only immune cells were labelled in the mammary gland stroma, findings that agree with previous studies.

      Major comments:

      1. It is rather difficult to conclude whether the observed nipple phenotype reflects an early embryonic/prepubertal defect in establishing the nipple stroma, is caused by a constitutive response to ongoing cell death, or a response to continuous DTA expression (or a combination of some of these). The data raise a couple of additional questions: Is there a nipple phenotype at 3 wk of age? It would not be totally unsurprising if ablation of a major fraction of dermal fibroblasts in the nipple area would lead to an early embryonic/prepubertal phenotype but there is no data on this. Hence, is there a "congenital" nipple deformity, as concluded by the authors (line 191)? Are there S100a4+ cells in the nipple area of pubertal S100a4-Cre/DTA mice? I.e. is there a continuous supply of new S100a4+ cells and thereby continuous cell death and DTA expression as one might expect based on the RNA-seq data?
      2. The subtitle on line 54 implies that that S100a4-Cre/DTA mice display a branching phenotype. However, it looks to me as if there is a pubertal outgrowth defect (as is also written in the body text, line 64) rather than a branching phenotype, potentially reflecting the much smaller size of S100a4-Cre/DTA mice (Fig. 2a). Unless there is a change in branch point frequency, I suggest rephrasing the title and discussion. Instead, I suggest the authors discuss the observed outgrowth delay considering the gross overall growth defect (Fig. 2a). If ductal outgrowth was normalized to the overall growth defect, would one still observe 'a delay in branching morphogenesis'?
      3. Fig. 4e shows Masson's Trichrome and Picrosirius Red staining and the authors report the findings as follows (lines 120-124): "collagen fibers were loosened in the DTA nipples and more densely packed in the S100a4-Cre;DTA nipples". Perhaps the authors could help non-specialists to observe the loosened fibers and if they wish to make quantitative statements ("more densely packed"), such statements should be backed-up by quantifications.
      4. I found the Discussion on the various mouse models somewhat problematic. Overall, the paper is written is a way that it often remains unclear whether it refers to studies addressing the role of S100a4 itself, studies addressing the function of S100a4+ cells via ablation approaches (S100a4-Cre or S100a4-CreERT2 crossed with floxed DTA), or those where S100a4-Cre has been used to delete gene X/Y/Z. These are all very different experimental approaches where one approach is not necessarily informative when trying to understand the results from another one. The authors should make these points clear and consider whether all their discussion points are relevant. The abstract states S100a4 (fibroblast-specific protein 1) is "expressed by mesenchymal cells and has been implicated in the development of eccrine glands, hair follicles, and mammary branching morphogenesis". However, the study on eccrine glands (ref. 19) shows that S100A4+ cells play a role in eccrine gland development but it does not address the role of S100a4 itself, while the study on hair follicles (ref.20) in turn reports the expression pattern of S100a4 in hair follicles but does not address its function, nor the role of S100a4+ cells. Finally, I failed to find references in the paper to studies addressing the role of S100a4, or S100a4+ cells in the mammary gland. Instead, the paper had references to studies where S100A4-Cre had been used to delete different genes and these mice had various mammary phenotypes - which, as indicated above, is a very different approach compared to deleting S100a4 or ablating S100a4+ cells.

      In Discussion (lines 197-200), the authors write: "We described significant delay in mammary branching morphogenesis in puberty, confirming an important role for S100A4+ cells in mammary development, as it was previously described (refs 37-39)." It should be noted that none of these studies addressed the role of S100A4+ cells:

      • Ref 37 used S100a4-Cre to delete sharpin
      • Ref 38 used the same Cre line to delete Ptch1, did not address the role of S100a4 or S100a4 expressing cells
      • Likewise ref 39 deleted another gene using S100a4-Cre

      Later on in Discussion, the authors compare the reported phenotype to previous studies (lines 248-255): "...targeting S100A4+ cells through knockout experiments can result in severe phenotypes, such as a reduction in adipose tissue (ref 26), skin phenotypes, a disrupted estrous cycle, reduced fertility (ref. 38), and complete infertility, hypogonadism and defects in pituitary endocrine function (ref. 28). Of these, Ref. 26 used the same approach as the current study (S100a4-Cre; DTA) (Fig. 7A in the paper) - these mice were significantly lean, with markedly reduced fat compared with the control mice - also the mice in the current study are very small, so perhaps they could also be described as 'lean'. Yet ref. 26 reports that female mice had comparable food uptake, respiratory exchange ratio and physical activity, and slightly increased energy expenditure

      Ref. 38 (as mentioned above) reports deletion of Ptch1 using S100a4-Cre lines and these mice "displayed a disrupted estrous cycle and dramatically reduced fertility over 6.5 weeks". However, this has nothing to do with the approaches where Fsp1/S100a4+ cells are depleted with DTA. Likewise, reference 28 analyzed the phenotype of S00a4-Cre;Ptch1fl/fl mice. Obviously, deleting Ptch1 using S100a4-Cre mice is quite a different approach than "targeting S100A4+ cells" through knockout experiments". Ptch1 deletion leads to a combination of gain-of-function (of Hedgehog activation) and loss-of-function (loss of Hh-independent functions of Ptch1) and hence comparisons with these phenotypes is rather challenging. I suggest the authors focus their phenotype comparisons to ref. 26 where S100a4/Fsp1+ cells were ablated with DTA, i.e. the same approach as in the current study. 5. I find the Discussion is somewhat off the topic by starting with WHO recommendations on breastfeeding and linking this to observed mouse phenotype. Overall, the discussion is rather long and from time-to-time more like a literature review. I would recommend keeping the Discussion more succinct and focused.

      Minor comments:

      Figure 5 would be more informative if it included more higher magnification images that would reveal the staining at the cellular level.

      Referee cross-comenting

      I agree with the comments of other reviewers. However, to me it seems that the analysis of S100a4 knockout mice would not be feasible within a reasonable timeframe and would represent a study of its own. My understanding was that the authors were not interested in S100a4 itself. Rather, S100a4-Cre was used as a tool to understand the importance of a certain (fibroblast) cell population for mammary gland morphogenesis.

      Significance

      General assessment:

      This study reveals the importance of the S100a4+ cell lineage for nipple formation while showing the same cells are dispensable for mammary gland morphogenesis. The main limitation is that it remains unclear whether the observed nipple phenotype is derived from an early embryonic/prepubertal defect in establishing the nipple stroma, is caused by a constitutive response to ongoing cell death, or a response to continuous DTA expression (or a combination of some of these). Hence its relevance as a model of human inverted nipple condition remains rather speculative.

      Advance:

      This study provides novel information on nipple morphogenesis, with potential (though with reservation) relevance to the congenital human inverted nipple condition affecting 3-5% of women.

      Audience:

      This work should appeal to mammary gland biologists interested in mammary gland development and nipple formation; those with interest on fibroblasts biology given that S100a4 was once thought to be a broad marker of fibroblasts, as well as those with interest in the inverted nipple condition.

      My expertise:

      Mammary gland morphogenesis, developmental biology, cell signaling

    1. “that’s horrible, so I’ll respond with an angry face emoji”

      This actually minds me of the recommendation algorithms discussed in the previous chapter. If a lot of people reply angry faces and platforms count that, they would let this post to reach more people. Some of them might also dislike that post and reply an angry face, which keeps the post spreading.

    1. Adapted from 'ORE Benefits InfographicLinks to an external site. (external site, opens in a new tab)' by Open Research EuropeLinks to an external site. (external site, opens in a new tab), licensed under CC BYLinks to an external site. (external site, opens in a new tab). Colours and layout changed. Open image in a new tab (opens in a new tab)

      The 'ORE Benefits Infographic' link from the original course no longer works. May we point it to https://opusproject.eu/openscience-news/maximizing-research-impact-traditional-metrics-and-real-world-influence/ instead?

      Updated attribution: Adapted from ‘ORE Benefits Infographic’ by Open Research Europe, licensed under CC BY 4.0. Colours and layout changed.

    2. Adapted from: whyopenresearch.orgLinks to an external site. (external site, opens in a new tab). Open image in a new tab (opens in a new tab) Text version: the benefits of open access

      We've removed the link to whyopenresearch.org as it pointed out to a casino website . May we change this credit to: 'Benefits of Open Access' by Danny Kingsley & Sarah Brown is licensed under CC BY 4.0?

    1. “It is cruel to refuse shelter to refugees”, says the editorial. “But it is much more cruel to make people refugees”.

      In other words, to make them into refugees → the refugee status is not given, but made.

    1. Welcome to the Beginner Pāḷi Course. Fourteen classes and a final project take you from the alphabet to reading the suttas in their own language, with a dictionary at your side.

      βξηβξηβξβξβξκ

    1. The ontology of the Norths-works is not entirely embodied in their material apparatus. Instead, perceivers are required complete the network

      DUBBIO Si dice che i percettori (il pubblico) completino le opere con la loro presenza. Si dice «At present, non of the Norths-works are interactive in the sense hat visitors' behaviour alters the sound or behaviour of the piece. However, perceptual activity, intention, and wilful decision-making shape visitors' experiences, rendering them immediate participants in these environment» e ancora «Each piece, in its own way, becomes a site for an entanglement of agency and identity between visitors and the piece that constitutes intra-activity» citando Karen Barad (2007).

      L'interattività deriverebbe dalla cognizione dei partecipanti, dalle loro scelte su come esperire i lavori, dalla loro riflessione critica della loro stessa posizione rispetto alle opere. Tuttavia, questa è una possibilità sempre aperta nell'arte. Da qui, secondo me, la necessità di differenziare le opere operativamente interattive da quelle intra-attive.

    2. encouraging

      DOMANDA come si incoraggia il pubblico a contemplare la propria percezione del fenomeno, del dato del fenomeno e di se stesso rispetto all'opera e al fenomeno a cui l'opera rimanda? VEDERE DOPO...

    3. error

      Chi esperisce "fa parte" dell'ambiente esperito. Anche i dati sono raccolti da una prospettiva

      «it is an error to assume that the experiencer and the experienced environment are separate from each another. This proposition is materialised by inviting visitors to consider that all data is gathered from a perspective. This proposition becomes more robustly phenomenological with the further consideration that all perception whatsoever arises from a perspective»

    4. fundamental disconnection

      Il lavoro si basa sulla disconnessione tra il fenomeno e il dato prodotto sul fenomeno, creando opere che "mimano" il fenomeno «some of the behaviours of the works exhibit a kind of uncanny mimicry in relation to an aspect of the subject matter of the piece, or to a natural environment [.] This draws attention to a fundamental disconnection between perceptions of data as a stand-in for the phenomenon it measures and the phenomenon itself, a subject discussed later in this section»

    5. Although the works sonify data, the data cannot be ‘read’ from the works as one reads measurements from a graph. Instead, the works use sound and data to create new environmental behaviours and dynamics that inspire laughter, curiosity, or frustration.

      Sonificazione non intelleggibile, ma lavoro comportamentale (Ascott) per ispirare reazioni nel pubblico

      «Although the works sonify data, the data cannot be ‘read’ from the works as one reads measurements from a graph. Instead, the works use sound and data to create new environmental behaviours and dynamics that inspire laughter, curiosity, or frustration»

    1. eLife Assessment

      This useful manuscript presents evidence that the major contribution of the lysine methyltransferase KMT2A to ribosomal gene transcription regulation is through promoting histone acetylation rather than H3K4 methylation, in part through interactions of KMT2A with PCAF. Some of the claims in the paper are unsupported by the evidence and therefore viewed as incomplete. Additional experiments demonstrating a more direct and causal link between PCAF and KMT2A would elevate the key conceptual advance that this new axis influences epigenetic pathways through an unconventional histone acetylation pathway. This work will be of interest to those studying transcriptional regulation and chromatin remodeling.

    2. Reviewer #1 (Public review):

      Summary:

      In this manuscript the authors study the distribution of the KMT2A methytransferase across the human ribosomal gene loci (rDNA) and the effects of its depletion on rDNA chromatin modification using a KMT2A conditional cell line. They used ChIP mapping data and claim to show that KMT2A depletion does not affect H3K4me3 levels but does reduce acetylation of H3K9 and reduces PCAF (KAT2B) binding. They further claim that KMT2A depletion causes stalling of RNA Polymerase I at the upstream "Spacer" promoter duplication on the rDNA.

      Weaknesses:

      I found the manuscript very confusing. In particular, I found it short on short on experimental detail and I found the data in general to be unconvincing. It is my opinion that the authors overinterpret their data, leading to unrealistic and unsupported claims. The manuscript also avoided discussing relevant recent studies on the regulation of the rDNA and of rDNA chromatin, most citations being to data 15 to 20 or more years old. This is most apparent when discussing the factors such as UBTF and SL1 that determine preinitiation complex assembly and the status of active versus inactive rDNA chromatin. Both have seen significant advances in recent years, and these should be thoroughly discussed.

      Specific comments:

      The authors do not describe the generation of the KMT2A conditional HEK293T cells or their treatment. Since these cells form the basis for subsequent experiments, they must be explained fully. I found that the conditional cell line had already been used in a previous unrelated publication "Chinchole et al. 2022". This manuscript explained that the KMT2A knockout is induced via Dox controlled Cas9 in a HEK cell clone constitutively expressing guide RNAs to KMT2A. The KMT2A knockout requires a 7 day treatment with Dox, leaving wide open the question of direct versus indirect effects of KMT2A loss, and perhaps explaining some of the discrepancies with published data.

      In mapping the interactions of KMT2A across the rDNA by ChIP they use 18 PCR amplicons, some of which the authors inaccurately state as targeting specific rDNA elements. For example, they state Primers (that is, Amplicons) #1-3 target the spacer and core 47S promoters. In fact, amplicon #2 overlaps neither of these promoters and lies centrally within the 1kbp of enhancer repeats between them. Exactly why emphasis is put on KMT2A chipping at these two promoters is anyhow questionable since KMT2A levels (Figure 1) appear equally high at multiple IGS amplicons.

      Discrepancies with published data are particularly apparent in their mapping of H3K4me2, H3K4me3 and H3K9ac, all directly relevant to their conclusions. These modifications have been shown in several publications to map to a single major site a few hundred bp upstream of the spacer promoter in multiple human and mouse cell types. Since the authors fail to monitor H3K4 methylation and H3K9 acetylation at this dominant site they also fail to provide significant evidence that KMT2A in fact affects H3 methylation or H3 acetylation on the rDNA.

      The KMT2A deletion drastically reduces PolI and UBF (UBTF) engagement across the rDNA, and consistent with this, SL1 and RRN3 engagement is reduced as are 47S levels. Thus, whatever the 7-day KMT2A deletion is doing to the cells it is certainly inducing a very significant shut-down of active rDNA. How such an extreme experimental model can tell anything of KMT2A functions is unclear.

      In the last results section, the authors further claim "Loss of KMT2A results in a stalled RNA Pol I complex at the spacer promoter" (Figure 4 and S4). To support this the authors try to map spacer promoter transcripts but mistakenly claim that the amplicon #1 can detect these transcripts. In fact, this amplicon overlaps the Spacer Promoter initiation site and so could not detect transcripts coming from this promoter. Published data, not cited, have demonstrated that PolI stalling at the spacer promoter occurs as a normal phenomenon on the active rDNA. Its loss is associated with suppression, not the opposite as inferred in this manuscript.

    3. Reviewer #2 (Public review):

      Summary:

      This important manuscript presents solid evidence that the major contribution of KMT2A to ribosomal gene transcription is through promoting histone acetylation rather than H3K4 methylation. It is suggested this occurs in part through interactions of KMT2A with PCAF.

      Strengths:

      This manuscript advances an important concept that chromatin modifying enzymes can also function to promote transcription through interactions with other transcriptional regulators independently of their catalytic functions. The data are overall supportive of this conclusion for KDM2A.

      Weaknesses:

      (1) Care needs to be taken regarding interpreting the lack of effect of depleting KMTs or HATs on histone acetylation or methylation levels. There are several overlapping HATS or KMTs that generate these modifications. The most that can be said from these negative results is that the depleted HAT or KMT is not the sole enzyme generating these modifications at these loci.

      (2) The biochemistry supporting an interaction between KDM2A and PCAF is under-developed and not strong enough to support this conclusion at this point.

      (3) It is concluded that acetylation causes increased rDNA transcription. However, acetylation may be a consequence of transcription instead, especially over transcribed regions.

      (4) Some conclusions, for example implicating PCAF, are based on overexpression experiments that are very artificial and difficult to draw conclusions from.

      (5) Several figures are generated using publicly available data sets that were reanalyze using updated reference genomes, sometimes from different cell lines/types. This seems to be a useful exercise but the methods should include information regarding the robustness of these data sets.

    4. Reviewer #3 (Public review):

      Summary:

      This manuscript examines a key mechanism by which KMT2A regulates RNA polymerase I transcription at ribosomal DNA. The authors suggest that KMT2A facilitates the recruitment of PCAF to rDNA. This process enhances histone acetylation and supports assembly of the RNA polymerase I transcription machinery. The study addresses an intriguing question by proposing a catalytic-independent role for KMT2A in rDNA transcription, potentially linking the KMT2A-dependent pathway to histone acetylation. However, several aspects of the proposed mechanism require further experimental validation. Notably, while the current evidence demonstrates associations among KMT2A, PCAF occupancy, histone acetylation, and rRNA transcription, it does not fully support the proposed objectives.

      Strengths:

      This could be the first report of a lysine methyltransferase regulating an acetyltransferase in Pol I-mediated gene expression.

      Weaknesses:

      (1) The causal relationship between KMT2A and the recruitment of PCAF needs to be demonstrated. Simply showing reduced PCAF occupancy after depleting KMT2A does not, on its own, prove direct recruitment. The authors should investigate whether KMT2A and PCAF physically interact, and whether this interaction depends on DNA or RNA.

      (2) The main prediction of the model is that restoring PCAF activity should reverse the molecular effects caused by the depletion of KMT2A. The authors should test whether PCAF restoration can mitigate the consequences of KMT2A loss. They should also assess PCAF occupancy, H3K9 acetylation levels, Pol I occupancy, and nascent rRNA synthesis.

      (3) The catalytic-independent function of KMT2A should be directly investigated. The absence of a significant change in H3K4me3 levels following KMT2A depletion does not confirm that KMT2A's catalytic activity is not essential. Rescue experiments comparing wild-type KMT2A with a catalytically inactive variant would provide a much stronger assessment of the proposed non-catalytic mechanism.

      (4) The manuscript highlights H3K9ac; however, various acetyltransferases and histone acetylation marks are linked to active rDNA. The authors should investigate whether KMT2A specifically regulates H3K9ac or leads to broader changes in histone acetylation at rDNA.

      (5) Due to the highly repetitive nature of rDNA, the manuscript must include detailed information about the reference assembly, mapping strategy, management of multimapping reads, normalisation processes, and biological replicates. The authors should demonstrate that any observed changes in occupancy are not due to technical differences in rDNA mapping or variations in copy number.

      (6) KMT2A regulates a wide range of transcriptional programs, and its depletion can affect proliferation, cell-cycle distribution, or nucleolar function. The authors should include appropriate controls to demonstrate that the rDNA phenotype is not a result of changes in cellular proliferation or a generalised response to transcriptional or nucleolar stress.

      (7) The CSB and B-WICH complex have previously been linked to the recruitment of PCAF and the acetylation of H3K9 at active rDNA. The authors should investigate whether KMT2A operates independently of these factors or if KMT2A functions within an established pathway for remodeling rDNA chromatin.

      (8) The distinction between active and silent rDNA should be taken into account. The authors need to determine whether KMT2A/PCAF occupancy is preferentially associated with active rDNA copies. Sequential ChIP analysis or the use of established markers for active, poised, and silent rDNA would help clarify the chromatin context in which the proposed mechanism operates.

    5. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript the authors study the distribution of the KMT2A methyltransferase across the human ribosomal gene loci (rDNA) and the effects of its depletion on rDNA chromatin modification using a KMT2A conditional cell line. They used ChIP mapping data and claim to show that KMT2A depletion does not affect H3K4me3 levels but does reduce acetylation of H3K9 and reduces PCAF (KAT2B) binding. They further claim that KMT2A depletion causes stalling of RNA Polymerase I at the upstream "Spacer" promoter duplication on the rDNA.

      Weaknesses:

      I found the manuscript very confusing. In particular, I found it short on short on experimental detail and I found the data in general to be unconvincing. It is my opinion that the authors overinterpret their data, leading to unrealistic and unsupported claims. The manuscript also avoided discussing relevant recent studies on the regulation of the rDNA and of rDNA chromatin, most citations being to data 15 to 20 or more years old. This is most apparent when discussing the factors such as UBTF and SL1 that determine preinitiation complex assembly and the status of active versus inactive rDNA chromatin. Both have seen significant advances in recent years, and these should be thoroughly discussed.

      Specific comments:

      The authors do not describe the generation of the KMT2A conditional HEK293T cells or their treatment. Since these cells form the basis for subsequent experiments, they must be explained fully. I found that the conditional cell line had already been used in a previous unrelated publication "Chinchole et al. 2022". This manuscript explained that the KMT2A knockout is induced via Dox-controlled Cas9 in a HEK cell clone constitutively expressing guide RNAs to KMT2A. The KMT2A knockout requires a 7-day treatment with Dox, leaving wide open the question of direct versus indirect effects of KMT2A loss, and perhaps explaining some of the discrepancies with published data.

      We thank the Reviewer for their comment. As the Reviewer correctly states, these KMT2A knockouts have been used in previous studies: Chinchole et al., 2022; PMID: 36111497 and Malik et al., 2023; PMID: 37379335. In both cases, the generation of KMT2A inducible knockout was described in detail in the Methods section; hence, in this manuscript we referred to these publications. However, if the Reviewer feels that details should still be provided, we will do so again here.

      As the Reviewer is aware, KMT2A is an essential chromatin-bound protein, which is quite stable in its heterodimer form. As we described in our publication, Malik et al., 2023, the inducible knockout clones were obtained after extensive screening and thoroughly characterized. While the protein diminishes in the WB after 3 days, we thought it necessary to confirm that it does not persist on the chromatin in ChIP assays.

      We are pasting the excerpt from the manuscript describing the relevant section below.

      “Our initial attempts to generate MLL knock-outs in several different cell lines were not successful. Therefore, keeping in mind that MLL is essential for cell viability and growth [26,27], we generated and successfully obtained knock-outs in HEK-293 cell lines using doxycycline-inducible Cas9 expression vectors ([52]; Fig 3A). MLL levels were drastically reduced in the 2 independent MLL-knock-out clones shown here (MLL iKO#11 and iKO#20) and both KOs showed cell cycle profile similar to Control cells (S3A Fig). In the case of chromatin-binding proteins, often cellular levels show reduction but not the chromatin-bound fraction. We, therefore, confirmed that MLL chromatin binding was indeed reduced in our inducible knock-out cell lines by ChIP assays (Figs 3B and S3B), both on centromere and HOXA9 promoter”.

      Inducible Cas9-mediated knockout provides an opportunity to generate irreversible knockouts, allowing sufficient time to deplete stable proteins and reveal delayed phenotypes or compensatory responses [PMID: 29036671]. However, this advantage has to be balanced with the potential secondary effects arising from prolonged depletion. Indeed, inducible Cas9 knockouts have been generated after similar induction periods by other laboratories to deplete chromatin-bound proteins [PMID: 28216383; PMID: 37702151], especially for members of the KMT2 family [PMID: 40846851, PMID: 27535106; PMID: 37535603 ]. Further, we have not observed any discrepancy with published data for KMT2A.

      In mapping the interactions of KMT2A across the rDNA by ChIP they use 18 PCR amplicons, some of which the authors inaccurately state as targeting specific rDNA elements. For example, they state Primers (that is, Amplicons) #1-3 target the spacer and core 47S promoters. In fact, amplicon #2 overlaps neither of these promoters and lies centrally within the 1kbp of enhancer repeats between them. Exactly why emphasis is put on KMT2A chipping at these two promoters is anyhow questionable since KMT2A levels (Figure 1) appear equally high at multiple IGS amplicons.

      As the enhancer repeats (ER) are clearly shown in the schematic of Fig. 1A and SF1B, and the same is stated in the legend of SF1B, rather than an inaccuracy, it is a matter of oversight. We thank the Reviewer for noticing this and bringing it to our attention. We will correct it in the text.

      We thank the Reviewer for raising this point regarding our focus on the promoters in the present study and the binding of KMT2A to the IGS region. KMT2A’s role in regulating RNA polymerase II transcription by regulating gene promoters is well established. However, its role in regulating RNA polymerase I transcription at the rDNA promoter has not been characterized. In this study, we provide evidence for a previously unrecognized role of KMT2A in regulating rDNA transcription and hence extend its function to regulation of RNA polymerase I.

      We agree that our ChIP data show KMT2A occupancy at multiple regions within the IGS, in addition to the promoter regions. For this reason, we felt it was important to present these data and perform ChIP-seq analyses of the rDNA locus to reveal the complete picture.

      Our emphasis on the promoter regions is aimed at understanding the potential role of KMT2A in regulating RNA polymerase I transcription initiation. However, we recognize that the enrichment of KMT2A at multiple IGS regions raises the possibility that it may have additional functions beyond promoter regulation. We are currently investigating the role of KMT2A at the IGS, and in our future studies we hope to address the functional significance of this occupancy in greater detail.

      Discrepancies with published data are particularly apparent in their mapping of H3K4me2, H3K4me3 and H3K9ac, all directly relevant to their conclusions. These modifications have been shown in several publications to map to a single major site a few hundred bp upstream of the spacer promoter in multiple human and mouse cell types. Since the authors fail to monitor H3K4 methylation and H3K9 acetylation at this dominant site they also fail to provide significant evidence that KMT2A in fact affects H3 methylation or H3 acetylation on the rDNA.

      We thank the reviewer for raising this important point about the distribution of histone modifications at the rDNA locus and how they depend on KMT2A. Previous studies have established that active histone modifications are enriched at specific regions of rDNA. In particular, landmark studies from two groups have pioneered the use of genome assemblies to map these histone modifications. The Scacheri group [Zentner et al. 2011, PMID: 21355038] reported enrichment of H3K4me2, H3K4me3, and H3K9ac at the human rDNA promoter, along with additional active-chromatin-associated enrichment within the intergenic spacer. Subsequently, the same group demonstrated that the distribution of histone modifications in mouse embryonic stem cells differs from that in human embryonic stem cells and other differentiated mouse cell types [Zentner et al. 2014, PMID: 24347625]. These findings underscore the importance of examining the relevant regulatory regions in the appropriate biological system.

      Similarly, studies from the Moss group radically redefine our understanding of how histone modifications are distributed across rDNA in mammals. They demonstrated that active histone modifications (including H3K4me2/3, H3K9ac, H3K27ac, and H3K36me3) are not present at the core 47S promoter or along the transcribed gene body of active rDNA repeats, but are instead strictly sequestered to a highly localized "Enhancer Boundary Complex" located a few hundred base pairs upstream of the spacer promoter. Consequently, this precise upstream hub represents the sole site of active histone modifications on active rDNA genes [Herdman et al., 2017, PMID: 28715449 ; Mars et al., 2018, PMID: 29158335]. These findings challenge the classical models of histone modification distribution at rDNA.

      However, an extensive body of classical literature presents a complementary view where active histone modifications directly decorate the core 47S promoter and proximal transcribed regions to dynamically coordinate RNA Polymerase I transcription. Work from the Grummt and Santoro laboratories established that unmethylated, active rDNA repeats possess prominent H3K4me3 and hyperacetylated histones directly at the core 47S promoter, serving as a functional "poised" platform for pre-initiation complex (PIC) assembly [Santoro and Grummt, 2002, PMID: 12368916; Santoro and Grummt, 2005, PMID: 15767661; Mayer et al., 2006, PMID: 16678107; Schmitz et al., 2010, PMID: 20952535]. Furthermore, tracking chromatin remodelling complexes—such as the CSB-PCAF complex investigated by the different groups—has shown that active depletion of histone acetyltransferases leads to a direct loss of H3K9ac specifically at the primary 47S initiation site, causing a failure in Pol I loading [Shen et al., 2013, PMID: 23667505; Vintermist et al., 2011, PMID: 21559432]. Besides these reports, a wealth of independent functional data highlights that key chromatin regulators dynamically target and modulate the core 47S promoter region to dictate ribosomal transcription initiation. Some examples are referred to here [Xei et al., 2012, PMID: 22570494; Wang et al., 2011, PMID: 21960006; Zhu et al., 2010, PMID: 20531378; Frescas et al., 2007, PMID: 17994099].

      Our investigation of KMT2A directly builds upon this functional promoter-centric model. While we appreciate that mapping variations exist between custom rDNA reference models in the literature, our data show clear and reproducible levels of H3 methylation and acetylation dynamics across the primary regulatory sites (including the 47S promoter and adjacent transcribed unit) upon KMT2A depletion. These changes correlate directly with functional changes in 47S pre-rRNA output, supporting a model where KMT2A-mediated histone modifications operate directly at the core transcription initiation machinery to regulate overall nucleolar output.

      We acknowledge that the distribution of histone modifications across the rDNA repeat and their functional dependence on KMT2A represent distinct questions. Accordingly, we interpret the ChIP-seq profiles in conjunction with our site-specific ChIP-qPCR and functional experiments rather than relying exclusively on the genome-wide mapping data. We therefore conclude that our results support a role for KMT2A in regulating the acetylation-associated chromatin environment and transcriptional activity of the rDNA promoter, without claiming that KMT2A depletion reduces all active histone modifications across the locus.

      We will clarify the relevant analysis and the scope of our conclusions in the revised manuscript.

      The KMT2A deletion drastically reduces PolI and UBF (UBTF) engagement across the rDNA, and consistent with this, SL1 and RRN3 engagement is reduced as are 47S levels. Thus, whatever the 7-day KMT2A deletion is doing to the cells it is certainly inducing a very significant shut-down of active rDNA. How such an extreme experimental model can tell anything of KMT2A functions is unclear.

      We feel that this concern is on similar lines as that stated in comment 1. We have already explained our inducible knockout model above. The cell-cycle profile of KMT2A-inducible knockout (iKO) cells reported in Malik et al. (2023), PMID: 37379335. provides relevant context for interpreting these effects.

      We further wish to highlight that contrary to expectation, the H3K4me2/3 levels are largely unaffected on the rDNA in KMT2A iKO cells, which is surprising but not unexpected given the role of KMT2A in selectively trimethylating specific gene loci (PMID: 19703992). However, the loss of acetylation on H3 is very specific to the study at hand.

      Unchanged H3K4me2/3 levels, along with selectively decreased acetylation, have not been reported for a general shutdown of active rDNA under extreme conditions like serum starvation etc. In case of a non-specific effect, both active methylation and acetylation marks are expected to go down. The presence of the H3K4me2/3 mark in our iKO cells acts as an internal control, confirming that the loss of H3 acetylation is a direct consequence of KMT2A depletion.

      Furthermore, in the current manuscript, the effects of KMT2A deletion on the Pol I transcriptional machinery can be rescued by the KMT2A catalytic point mutant carrying the N3906A mutation but not by the mutant with TAD deletion. This rescue provides important evidence that the phenotype is linked to a specific function of KMT2A rather than being a nonspecific consequence of KMT2A deletion.

      Our findings, considered alongside Lone et al. (2026) PMID: 42096476, support distinct roles for KMT2A and KMT2F in regulating rDNA transcription. In particular, the effects of KMT2A loss occur without a substantial reduction in rDNA H3K4me3, distinguishing its role from the H3K4me3-dependent function of KMT2F in promoting Pol I pre-initiation complex formation.

      In the last results section, the authors further claim "Loss of KMT2A results in a stalled RNA Pol I complex at the spacer promoter" (Figure 4 and S4). To support this the authors try to map spacer promoter transcripts but mistakenly claim that the amplicon #1 can detect these transcripts. In fact, this amplicon overlaps the Spacer Promoter initiation site and so could not detect transcripts coming from this promoter. Published data, not cited, have demonstrated that PolI stalling at the spacer promoter occurs as a normal phenomenon on the active rDNA. Its loss is associated with suppression, not the opposite as inferred in this manuscript.

      We thank the Reviewer for raising this point. We have double-checked that amplicon #1 is downstream of the spacer promoter start site. However, since the reviewer has raised this point, we will look into it in greater detail and remap our amplicons. We will also revisit the previous literature and compare our results with the published findings.

      Reviewer #2 (Public review):

      Summary:

      This important manuscript presents solid evidence that the major contribution of KMT2A to ribosomal gene transcription is through promoting histone acetylation rather than H3K4 methylation. It is suggested this occurs in part through interactions of KMT2A with PCAF.

      Strengths:

      This manuscript advances an important concept that chromatin-modifying enzymes can also function to promote transcription through interactions with other transcriptional regulators independently of their catalytic functions. The data are overall supportive of this conclusion for KDM2A.

      Weaknesses:

      (1) Care needs to be taken regarding interpreting the lack of effect of depleting KMTs or HATs on histone acetylation or methylation levels. There are several overlapping HATS or KMTs that generate these modifications. The most that can be said from these negative results is that the depleted HAT or KMT is not the sole enzyme generating these modifications at these loci.

      We thank the Reviewer for this important comment. We agree that histone modifications are regulated by multiple enzymes with overlapping or partially redundant activities, and that the absence of a detectable change following depletion of an individual KMT or KAT does not exclude a contribution from that enzyme to the regulation of the modification.

      Specifically, the lack of a detectable reduction in H3K4me3 following KMT2A depletion indicates that KMT2A depletion alone is insufficient to produce a detectable decrease in H3K4me3 under the conditions examined. The persistence of this mark may reflect the activity of other KMTs and hence functional redundancy. In contrast, our observation that KMT2A depletion reduces H3K9 acetylation, together with our findings implicating PCAF in KMT2A-dependent rDNA transcription, supports a role for KMT2A in regulating an acetylation-associated mechanism at this locus.

      Nevertheless, we acknowledge that these findings do not, by themselves, establish that PCAF is the sole enzyme responsible for H3K9 acetylation or that it accounts for all KMT2A-dependent effects on rDNA transcription. We will revise the interpretation of our results to avoid attributing the maintenance or loss of a histone modification exclusively to a single enzyme.

      (2) The biochemistry supporting an interaction between KDM2A and PCAF is under-developed and not strong enough to support this conclusion at this point.

      We thank the reviewer for their comment. Based on these comments and those from Reviewer 3, we plan to add more experiments to strengthen the KMT2A-PCAF interaction, including a reverse pull-down.

      (3) It is concluded that acetylation causes increased rDNA transcription. However, acetylation may be a consequence of transcription instead, especially over transcribed regions.

      We thank the reviewer for raising this important point regarding the relationship between histone acetylation and rDNA transcription. We agree that increased histone acetylation over transcriptionally active regions may either contribute to transcriptional activation or result from ongoing transcription. These possibilities are not mutually exclusive.

      Our study provides several lines of evidence supporting a functional relationship between KMT2A and PCAF-associated histone acetylation, which indicate that acetylation is the cause and not a consequence of rDNA transcription. KMT2A depletion reduces rDNA transcription and is accompanied by a reduction in H3K9 acetylation. In addition, we present a model in which KMT2A facilitates the transfer of RNA Pol I PIC from the spacer promoter to the 47S promoter following its assembly at the spacer promoter by recruiting PCAF to rDNA through its TA domain. Together, these observations support that acetylation supports efficient rDNA transcription

      Nevertheless, we acknowledge that these findings do not completely exclude the possibility that some of the observed reduction in histone acetylation occurs downstream of reduced transcription and will modify the discussion accordingly.

      (4) Some conclusions, for example implicating PCAF, are based on overexpression experiments that are very artificial and difficult to draw conclusions from.

      We thank the reviewer for their comment. We agree that overexpression experiments can produce non-physiological protein levels and potentially influence experimental readouts. However, our interpretation of PCAF involvement is based on a broader set of observations presented in this study, including the effects of KMT2A depletion on rDNA transcription and histone acetylation, together with our experiments examining the contribution of individual histone acetyltransferases to rRNA synthesis. These findings identify PCAF as a key co-activator of KMT2A-dependent transcriptional activation.

      Therefore, the overexpression experiments have been used to provide mechanistic support rather than definitive evidence that endogenous PCAF is the sole or essential mediator of this process.

      (5) Several figures are generated using publicly available data sets that were reanalyze using updated reference genomes, sometimes from different cell lines/types. This seems to be a useful exercise but the methods should include information regarding the robustness of these data sets.

      We thank the Reviewer for their concern; we will update this information in the Methods.

      Reviewer #3 (Public review):

      Summary:

      This manuscript examines a key mechanism by which KMT2A regulates RNA polymerase I transcription at ribosomal DNA. The authors suggest that KMT2A facilitates the recruitment of PCAF to rDNA. This process enhances histone acetylation and supports assembly of the RNA polymerase I transcription machinery. The study addresses an intriguing question by proposing a catalytic-independent role for KMT2A in rDNA transcription, potentially linking the KMT2A-dependent pathway to histone acetylation. However, several aspects of the proposed mechanism require further experimental validation. Notably, while the current evidence demonstrates associations among KMT2A, PCAF occupancy, histone acetylation, and rRNA transcription, it does not fully support the proposed objectives.

      Strengths:

      This could be the first report of a lysine methyltransferase regulating an acetyltransferase in Pol I-mediated gene expression.

      Weaknesses:

      (1) The causal relationship between KMT2A and the recruitment of PCAF needs to be demonstrated. Simply showing reduced PCAF occupancy after depleting KMT2A does not, on its own, prove direct recruitment. The authors should investigate whether KMT2A and PCAF physically interact, and whether this interaction depends on DNA or RNA.

      As we have shown that KMT2A and PCAF interact by endogenous IP (Fig. S4D) and pull-down (Fig. S4E) experiments with the KMT2A C-terminal fragment containing the TAD domain, does the Reviewer mean in vitro interaction? Both proteins are large and may be difficult to produce in bacteria.

      We do plan to perform reverse IP as suggested by Reviewer 2, and during these experiments, we can plan to test if this interaction is promoted by DNA or RNA by testing it in the presence or absence of DNase or RNase.

      (2) The main prediction of the model is that restoring PCAF activity should reverse the molecular effects caused by the depletion of KMT2A. The authors should test whether PCAF restoration can mitigate the consequences of KMT2A loss. They should also assess PCAF occupancy, H3K9 acetylation levels, Pol I occupancy, and nascent rRNA synthesis.

      We agree with the Reviewer that this is the central hypothesis of our study. Ideally, a simple global restoration or overexpression of PCAF should be able to rescue the effects caused by the depletion of KMT2A. However, based on our model and existing literature, we expect the following issues:

      (1) We propose that KMT2A serves as a recruiter, a physical scaffold, to target PCAF to the rDNA. Therefore, mere overexpression of PCAF, in the absence of KMT2A, will not be able to ensure its localisation to the rDNA locus.

      (2) Transcription activation by KMT2A involves tight synergy between its TA domain and specific KATs (PMID: 24253305; PMID: 31515273). The KMT2A TAD contains intrinsically disordered regions that must directly engage co-activator docking sites—specifically interacting with domains like the KIX domain of p300/CREB-binding protein acetyltransferase complexes—to transition into a stable secondary structure and drive downstream chromatin remodelling. Because this functional synergy requires the physical presence of both the KMT2A TAD and the KAT complex at the promoter, in our opinion, restoring PCAF activity in a KMT2A-depleted background cannot rescue localized H3K9 acetylation at the rDNA promoter. While we are not sure how PCAF engages with the KMT2A TAD domain, based on the above literature, the physical presence of KMT2A may be required.

      We will test this hypothesis by overexpressing PCAF in the absence (or presence) of KMT2A, monitoring rRNA expression, and then plan to undertake future experiments accordingly.

      (3) The catalytic-independent function of KMT2A should be directly investigated. The absence of a significant change in H3K4me3 levels following KMT2A depletion does not confirm that KMT2A's catalytic activity is not essential. Rescue experiments comparing wild-type KMT2A with a catalytically inactive variant would provide a much stronger assessment of the proposed non-catalytic mechanism.

      We thank the reviewer for their comment. We completely agree that it is important to rule out whether catalytic activity of KMT2A is essential for rRNA transcription. We have shown in Fig. 4G that the catalytic point mutant carrying the N3906A mutation is able to recruit PCAF and RNA Pol I to the rDNA but not the KMT2A∆TAD mutant. This rescue experiment supports our hypothesis that the catalytic activity of KMT2A is not involved in mediating transcription at rDNA, but TA domain-dependent recruitment of PCAF is critical.

      (4) The manuscript highlights H3K9ac; however, various acetyltransferases and histone acetylation marks are linked to active rDNA. The authors should investigate whether KMT2A specifically regulates H3K9ac or leads to broader changes in histone acetylation at rDNA.

      We thank the reviewer for their comment. This is indeed an insightful suggestion. As shown in Fig 3. Multiple histone acetylation marks decorate the rDNA, and it is not possible for us to test them all. We propose to test some of these marks during revision to gain insight into the range of KMT2A-mediated regulation.

      (5) Due to the highly repetitive nature of rDNA, the manuscript must include detailed information about the reference assembly, mapping strategy, management of multimapping reads, normalisation processes, and biological replicates. The authors should demonstrate that any observed changes in occupancy are not due to technical differences in rDNA mapping or variations in copy number.

      We thank the reviewer for their comment. As indicated in the manuscript, we have used the pipeline from George et al. al, 2023, PMID: 37121547, which describes all these parameters in detail. Hence, we have omitted a repetition here but added a brief sentence in the Methods now.

      (6) KMT2A regulates a wide range of transcriptional programs, and its depletion can affect proliferation, cell-cycle distribution, or nucleolar function. The authors should include appropriate controls to demonstrate that the rDNA phenotype is not a result of changes in cellular proliferation or a generalised response to transcriptional or nucleolar stress.

      We thank the reviewer for highlighting this important point. The transcriptional profiling recently published by our lab [Lone et al. (2026), PMID: 42096476; Malik et al., 2023, PMID: 37379335] provides clear evidence that helps distinguish direct regulatory effects from generalized stress responses. Our global transcript analysis indicates that KMT2A depletion specifically downregulates transcription at the rDNA repeats without inducing hallmark markers of nucleolar stress or broad cell-cycle arrest.

      (7) The CSB and B-WICH complex have previously been linked to the recruitment of PCAF and the acetylation of H3K9 at active rDNA. The authors should investigate whether KMT2A operates independently of these factors or if KMT2A functions within an established pathway for remodeling rDNA chromatin.

      We agree with the Reviewer that CSB and the B-WICH complex, PCAF recruitment, and H3K9ac deposition are indeed a well-documented axis at active rDNA. In the present study, our focus is on establishing the functional role of KMT2A at the rDNA locus. Given that KMT2A traditionally acts as a histone methyltransferase that often precedes or cooperates with acetyltransferases like PCAF, it is highly plausible that KMT2A operates sequentially or in concert with the B-WICH pathway to remodel chromatin.

      However, mapping these biochemical interactions is beyond our current aims. Nonetheless, we agree this is a crucial conceptual link, and we will expand this link in our Discussion.

      (8) The distinction between active and silent rDNA should be taken into account. The authors need to determine whether KMT2A/PCAF occupancy is preferentially associated with active rDNA copies. Sequential ChIP analysis or the use of established markers for active, poised, and silent rDNA would help clarify the chromatin context in which the proposed mechanism operates.

      We appreciate the Reviewer’s suggestion to determine if KMT2A/PCAF occupancy is restricted to active rDNA copies. While this is an important factor to consider, making a clear distinction is currently not possible due to several constraints. First, the highly repetitive nature of rDNA repeats makes it difficult to differentiate active from silent fractions both experimentally and bioinformatically. Second, performing sequential ChIP to definitively map this co-occupancy is technically challenging and establishing and optimising this protocol within the revision timeframe will be extremely difficult.

      Furthermore, the exact histone landscapes defining active, poised, and silent rDNA are not yet fully characterized; much of the existing consensus in epigenetic literature relies heavily on observations drawn from RNA Polymerase II systems rather than Pol I repeats. As demonstrated by Xie et al., 2012 [PMID: 22570494], rDNA promoters frequently feature bivalent marks—bearing both active H3K4me3 and repressive H3K27me3 modifications—that characterize a poised, non-transcribing state. This biological ambiguity makes absolute active/silent categorization extremely difficult in our case.

      References:

      (1) Chinchole A, Lone KA, Tyagi S. MLL regulates the actin cytoskeleton and cell migration by stabilising Rho GTPases via the expression of RhoGDI1. Journal of Cell Science. 2022;135(20):jcs260042. doi:10.1242/jcs.260042 PMID: 36111497

      (2) Frescas D, Guardavaccaro D, Bassermann F, Koyama-Nasu R, Pagano M. JHDM1B/FBXL10 is a nucleolar protein that represses transcription of ribosomal RNA genes. Nature. 2007;450(7167):309-313. doi:10.1038/nature06255 PMID: 17994099

      (3) Garg A, Orru R, Ye W, et al. Structural and mechanistic insights into the interaction of the circadian transcription factor BMAL1 with the KIX domain of the CREB-binding protein. J Biol Chem. 2019;294(45):16604-16619. doi:10.1074/jbc.RA119.009845 PMID: 31515273

      (4) George SS, Pimkin M, Paralkar VR. Construction and validation of customized genomes for human and mouse ribosomal DNA mapping. J Biol Chem. 2023;299(6):104766. doi:10.1016/j.jbc.2023.104766 PMID: 37121547

      (5) Herdman C, Mars JC, Stefanovsky VY, et al. A unique enhancer boundary complex on the mouse ribosomal RNA genes persists after loss of Rrn3 or UBF and the inactivation of RNA polymerase I transcription. PLoS Genet. 2017;13(7):e1006899. doi:10.1371/journal.pgen.1006899 PMID: 28715449

      (6) Kühn MWM, Song E, Feng Z, et al. Targeting Chromatin Regulators Inhibits Leukemogenic Gene Expression in NPM1 Mutant Leukemia. Cancer Discov. 2016;6(10):1166-1181. doi:10.1158/2159-8290.CD-16-0237 PMID: 27535106

      (7) Lone KA, Karole AM, Ravindran G, Tyagi S. The KMT2F histone methyltransferase interacts with the RNA polymerase I machinery to promote ribosomal RNA transcription. PLOS Biology. 2026;24(5):e3003785. doi:10.1371/journal.pbio.3003785 PMID: 42096476

      (8) Malik KK, Sridhara SC, Lone KA, Katariya PD, Pulimamidi D, Tyagi S. MLL methyltransferases regulate H3K4 methylation to ensure CENP-A assembly at human centromeres. PLOS Biology. 2023;21(6):e3002161. doi:10.1371/journal.pbio.3002161 PMID: 37379335

      (9) Mars JC, Sabourin-Felix M, Tremblay MG, Moss T. A Deconvolution Protocol for ChIP-Seq Reveals Analogous Enhancer Structures on the Mouse and Human Ribosomal RNA Genes. G3 Genes|Genomes|Genetics. 2018;8(1):303-314. doi:10.1534/g3.117.300225 PMID: 29158335

      (10) Mayer C, Schmitz KM, Li J, Grummt I, Santoro R. Intergenic Transcripts Regulate the Epigenetic State of rRNA Genes. Molecular Cell. 2006;22(3):351-361. doi:10.1016/j.molcel.2006.03.028 PMID: 16678107

      (11) McKinley KL, Cheeseman IM. Large-Scale Analysis of CRISPR/Cas9 Cell-Cycle Knockouts Reveals the Diversity of p53-Dependent Responses to Cell-Cycle Defects. Developmental Cell. 2017;40(4):405-420.e2. doi:10.1016/j.devcel.2017.01.012 PMID: 28216383

      (12) Ning M, Hoshii T, Nakagawa T, et al. Non-catalytic role of SETD1A promotes gastric cancer cell proliferation through the E2F4-TAF6 axis in the cell cycle. Cell Death Dis. 2025;16(1):639. doi:10.1038/s41419-025-07976-4 PMID: 40846851

      (13) Perlee S, Kikuchi S, Nakadai T, et al. SETD1A function in leukemia is mediated through interaction with mitotic regulators BuGZ/BUB3. EMBO Rep. 2023;24(10):e57108. doi:10.15252/embr.202357108 PMID: 37535603

      (14) Santoro R, Li J, Grummt I. The nucleolar remodeling complex NoRC mediates heterochromatin formation and silencing of ribosomal gene transcription. Nat Genet. 2002;32(3):393-396. doi:10.1038/ng1010 PMID: 12368916

      (15) Santoro R, Grummt I. Epigenetic mechanism of rRNA gene silencing: temporal order of NoRC-mediated histone modification, chromatin remodeling, and DNA methylation. Mol Cell Biol. 2005;25(7):2539-2546. doi:10.1128/MCB.25.7.2539-2546.2005 PMID: 15767661

      (16) Schmitz KM, Mayer C, Postepska A, Grummt I. Interaction of noncoding RNA with the rDNA promoter mediates recruitment of DNMT3b and silencing of rRNA genes. Genes Dev. 2010;24(20):2264-2269. doi:10.1101/gad.590910 PMID: 20952535

      (17) Shen M, Zhou T, Xie W, et al. The chromatin remodeling factor CSB recruits histone acetyltransferase PCAF to rRNA gene promoters in active state for transcription initiation. PLoS One. 2013;8(5):e62668. doi:10.1371/journal.pone.0062668 PMID: 23667505

      (18) Thakur JK, Yadav A, Yadav G. Molecular recognition by the KIX domain and its role in gene regulation. Nucleic Acids Res. 2014;42(4):2112-2125. doi:10.1093/nar/gkt1147 PMID: 24253305

      (19) van Schie JJM, de Lint K, Molenaar TM, et al. CRISPR screens in sister chromatid cohesion defective cells reveal PAXIP1-PAGR1 as regulator of chromatin association of cohesin. Nucleic Acids Res. 2023;51(18):9594-9609. doi:10.1093/nar/gkad756 PMID: 37702151

      (20) Vintermist A, Böhm S, Sadeghifar F, et al. The chromatin remodelling complex B-WICH changes the chromatin structure and recruits histone acetyl-transferases to active rRNA genes. PLoS One. 2011;6(4):e19184. doi:10.1371/journal.pone.0019184 PMID: 21559432

      (21) Wang P, Lin C, Smith ER, et al. Global Analysis of H3K4 Methylation Defines MLL Family Member Targets and Points to a Role for MLL1-Mediated H3K4 Methylation in the Regulation of Transcriptional Initiation by RNA Polymerase II. Molecular and Cellular Biology. 2009;29(22):6074-6085. doi:10.1128/MCB.00924-09 PMID: 19703992

      (22) Wang W, Chen Z, Mao Z, et al. Nucleolar protein Spindlin1 recognizes H3K4 methylation and stimulates the expression of rRNA genes. EMBO Rep. 2011;12(11):1160-1166. doi:10.1038/embor.2011.184 PMID: 21960006

      (23) Xie W, Ling T, Zhou Y, et al. The chromatin remodeling complex NuRD establishes the poised state of rRNA genes characterized by bivalent histone modifications and altered nucleosome positions. Proc Natl Acad Sci U S A. 2012;109(21):8161-8166. doi:10.1073/pnas.1201262109 PMID: 22570494

      (24) Yuen G, Khan FJ, Gao S, et al. CRISPR/Cas9-mediated gene knockout is insensitive to target copy number but is dependent on guide RNA potency and Cas9/sgRNA threshold expression level. Nucleic Acids Research. 2017;45(20):12039-12053. doi:10.1093/nar/gkx843 PMID: 29036671

      (25) Zentner GE, Saiakhova A, Manaenkov P, Adams MD, Scacheri PC. Integrative genomic analysis of human ribosomal DNA. Nucleic Acids Res. 2011;39(12):4949-4960. doi:10.1093/nar/gkq1326 , PMID: 21355038

      (26) Zentner GE, Balow SA, Scacheri PC. Genomic Characterization of the Mouse Ribosomal DNA Locus. G3 Genes|Genomes|Genetics. 2014;4(2):243-254. doi:10.1534/g3.113.009290 PMID: 24347625

      (27) Zhu Z, Wang Y, Li X, et al. PHF8 is a histone H3K9me2 demethylase regulating rRNA synthesis. Cell Res. 2010;20(7):794-801. doi:10.1038/cr.2010.75 PMID: 20531378

    1. eLife Assessment

      This study provides invaluable insights into the field of genomic prediction of traits by demonstrating how machine learning algorithms can integrate genomic and transcriptomic data and handle nonlinearity, providing important comparative prediction results across environments. The rationale and analyses conducted to integrate the two types of ~omics datasets in two environments are solid. The work will be of interest to plant geneticists and breeders, especially those interested in the genetics of environmental adaptation.

    2. Reviewer #2 (Public review):

      The authors examine the relative contributions of SNP and transcriptomic information to predicting trait variation and phenotypic plasticity in switchgrass. Using a diversity panel of 426 genotypes evaluated in Texas and Michigan, USA., this study demonstrates that transcriptomic predictors could outperform genomic markers in predicting environmental differences for traits like biomass yield and tiller count, highlighting the trait-dependent advantage of transcriptomic information over SNPs. However, combining SNPs and transcriptomic predictors generally yielded limited improvement in prediction accuracy.

      A principal limitation of this study concerns the experimental design and the robustness of transcriptomic predictors. With only one transcriptomic sample per genotype in each environment, within-genotype variation in transcript abundance cannot be estimated, making it difficult to distinguish genotype-specific expression responses from individual-plant variation. Unlike SNP genotypic information, which provides a stable representation of a genotype's inherited genetic variation underlying phenotypic differences, transcript abundance is highly sensitive to environmental conditions, developmental stages, and physiological state at the time of sampling. As a result, the stability of the transcriptomic predictors and their predictive performance across independent environments, years, or sampling conditions remain untested. Furthermore, the stability of feature selection across different training populations has not been established, raising additional questions about the reproducibility of the identified candidate genes. Together, these limitations constrain their generalizability and the biological interpretation of transcriptomic associations with phenotypic plasticity.

      Overall, this study provides valuable comparative evidence regarding the predictive performance of genomic and transcriptomic information. Its principal contribution lies in demonstrating that the relative predictive advantages of these information sources are trait-dependent and their integration does not necessarily improve prediction accuracy. While the predictive findings are supported, the biological interpretations extend beyond the evidence presented, as the identified transcriptomic predictors and their interactions do not establish causal genetic effects, epistasis, or direct regulatory relationships. Therefore, the proposed genetic mechanisms and environment-specific interactions should be regarded as hypotheses requiring independent biological validation, rather than established mechanistic findings.

    3. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      The study by Izquierdo and colleagues provides important insights into the field of genomic and transcriptomic prediction of traits across multiple environments. The rationale and analyses conducted to integrate the two types of ~omics datasets across two environments are solid. However, some clarification would be appreciated in the presentation of the results, and adding some statistical control to clarify how the predictors were selected, or assessing their importance using the SHAP framework, would further consolidate the findings.

      We appreciate the positive feedback and the constructive evaluation of our work. In response, we have added further clarification to the Results section and provided additional detail on the feature selection procedure to facilitate understanding of how predictors were identified within the modeling framework.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      P. Izquierdo et al. investigated the genetic determinism of various traits of interest in switchgrass using large-scale genomic and transcriptomic data. More specifically, they worked on a diversity panel comprising 426 genotypes evaluated in common-garden experiments at two locations (Michigan and Texas). The phenotypic and genomic data were already published. In this work, they produced transcriptomic data for each of the 426 genotypes at each site, and they carried out phenotype predictions using genomic and transcriptomic data separately or together. While they were moderately correlated at each location, both omic information appeared to be complementary for the prediction of phenotype. To further exploit the fact that they have data across two locations, they computed differences for phenotypes and transcripts between locations as indicators of trait and transcript plasticity, respectively. They built predictive models of trait plasticity using genomic information and transcript plasticity, which proved to be quite accurate for traits affected by GxE. Finally, they made use of SHAP values from predictive models of flowering time and biomass at each location, as well as for their plasticity, to gain insight into their genetic determinism. These SHAP values provide the importance of the predictive features (SNP and/or transcripts) for trait prediction. This allowed them to confirm some candidate genes and to propose new candidates for both traits.

      We appreciate the reviewer's thorough and constructive summary of our work. We would like to clarify that while the genomic data were previously published, the transcriptomic and phenotypic data were generated as part of the present study. We have revised the text to make this distinction explicit.

      Strengths:

      I found this study interesting and rich. I think the sample size (426 genotypes) is large enough to support the findings. The use of a modern machine-learning approach (XGBoost) together with SHAP indices to find interesting features and get insights into the biological mechanisms underlying flowering time and biomass production is quite original. The methodology employed is globally sound. I also like the fact that the authors accounted implicitly for the population structure by providing a baseline prediction using the first 5 PCs.

      Weaknesses:

      While the methodology is globally sound, I sometimes had difficulties following exactly what was done. This is partly due to the fact that the authors used 2 omics (SNPs and transcripts) to predict phenotypes, and sometimes, in the results, it is not clear which of the 2 is the focus. This was especially the case for the importance of the features and the interpretability of the models, where I found it sometimes hard to tell whether the analysis was done on SNPs or transcripts.

      We have revised the text throughout to clarify when models using SNP, transcript (T), or both data types (S+T) were the focus of the analysis.

      Also, regarding the methodology, I did not understand why the authors needed to perform a feature selection approach. Maybe it was required to perform the interaction analysis, which could not be deployed on all the features? But regarding the importance of the features, I do not get the added value of the selection over the direct use of SHAP indices when using all features. Maybe this is because I am not a specialist in this kind of approach, but maybe the authors could add more details to explain the rationale behind the feature selection.

      Feature selection was performed to identify the subset of features that optimized predictive accuracy while reducing model complexity. By retaining only the most informative features, the resulting models achieve higher predictive accuracy than models trained on all features, and model interpretation is simplified. The rationale and procedure are described.

      Regarding SHAP interactions specifically, the number of features used to estimate SHAP interaction scores was limited to 5,000 due to the high computational demands of this analysis. Interaction scores are estimated for each individual in the training population, producing a 5,000 × 5,000 interaction matrix per individual. With 306 individuals in the training population and 10 independent model runs, this corresponds to 3,060 matrices of this size.

      Reviewer #2 (Public review):

      Summary:

      The authors aimed to evaluate whether integrating genomic (SNP) and transcriptomic information with machine learning can improve phenotypic prediction of polygenic traits across environments. The manuscript explored not only the predictability across models and predictor feature sets, but also attempted to identify meaningful genes and interactions underlying trait variation.

      Strengths:

      The main strength of the manuscript is its integration of SNP, transcriptomic, and phenotype datasets for 426 sorghum genotypes between Texas and Michigan. It provides a systematic comparison of predictor types (SNP versus transcriptomic abundance) and model strategies to integrate them.

      Weaknesses:

      (1) Experimental Design

      The experimental design raises several concerns that should be clarified before strong biological conclusions are drawn from the transcriptomic analyses.

      First, the transcriptomic sampling is not well aligned with the developmental stages most relevant to the phenotypes being modeled. Leaf tissue was collected at a single time point in each environment, whereas traits such as flowering time, biomass, tiller count, and panicle height arise from developmental processes occurring over extended and potentially distinct temporal windows. Consequently, the measured expression profiles are likely to reflect physiological states specific to the sampling dates (May 5-6 in Texas and June 22-24 in Michigan) rather than the regulatory processes underlying the target phenotypes.

      We selected the pre-flowering stage because it represents a critical developmental transition during which plants remain largely vegetative while initiating reproductive development. Gene expression at this stage is therefore expected to capture regulatory processes associated with flowering initiation while also reflecting pathways involved in vegetative growth and biomass accumulation. In our previous work in maize (Azodi et al., 2020), we demonstrated that transcriptomic profiles obtained at a single developmental stage can contain information predictive of complex traits. Although the expression profiles analyzed here do not capture the full temporal dynamics underlying traits such as flowering time and biomass, they provide a biologically meaningful snapshot of molecular variation relevant to these traits. We have revised the manuscript to acknowledge this limitation and to clarify that our findings should be interpreted as associations between gene expression measured at the pre-flowering stage and the phenotypes of interest, rather than as evidence that all identified genes directly regulate these traits throughout development.

      Second, the phrase "haphazardly randomized" is questionable for a field experiment. It is unclear whether the design included formal randomization, blocking, row/column structure, or spatial correction. Without explicit accounting for spatial field heterogeneity, environmental variation within sites may confound genotype and transcriptomic effects.

      We thank the reviewer for this comment and agree that the original wording was unclear. The text has been revised to more precisely describe the experimental design. The establishment and management of the common gardens are described in detail in Lovell et al. (2021). Briefly, the diversity panel was established as a single-block planting at each location, with genotypes distributed throughout the field and arranged in a honeycomb design in which each individual had four nearest neighbors located 1.5 m apart. We have removed the phrase "haphazardly randomized," which was imprecise.

      Because only a single replicate of each genotype was established at each site, formal spatial correction was not performed, as genotype and within-site spatial effects cannot be estimated independently in this design. We acknowledge that additional replication would have improved our ability to account for fine-scale environmental heterogeneity.

      However, several aspects of the experimental design reduce the likelihood that spatial variation substantially confounded the results. The diversity panel is part of a coordinated common garden network spanning ten field sites across the United States, designed primarily to evaluate genotype-by-environment interactions across contrasting environments rather than within-site spatial variation. In addition, the AP13 reference genotype was distributed throughout the experimental plots and did not show evidence of substantial spatial heterogeneity at the Michigan site, suggesting that within-site environmental variation was limited. We have added text acknowledging this limitation and its implications for interpreting genotype and genotype-by-environment effects.

      Third, the Methods do not clearly describe biological replication for RNA-seq. If each genotype-by-environment combination were represented by a single transcriptomic sample, then within-genotype expression variance cannot be estimated. This is important because transcript abundance is highly sensitive to microenvironment, sampling time, tissue status, developmental stage, and technical variation. The absence of replication significantly weakens confidence in gene-level feature importance and gene-gene interaction claims.

      We appreciate this thoughtful comment and fully acknowledge that the absence of biological replication for RNA-seq within each genotype-by-environment combination is an important limitation of this study. Because the diversity panel consisted of a single plant per genotype at each field site, transcriptomic data were generated from one biological sample per genotype-by-environment combination, and within-genotype expression variance could not be directly estimated. We recognize that transcript abundance can be influenced by microenvironmental conditions, developmental stage, tissue status, sampling time, and technical variation, all of which may affect confidence in gene-level feature importance and inferred interaction effects.

      The scale of the experiment, involving 426 genotypes evaluated across contrasting common gardens, made biological replication for RNA-seq financially and logistically impractical. This experimental design was established to maximize representation of genetic diversity and environmental variation across a large multi-environment common garden network rather than to obtain replicated transcriptomic measurements within individual sites. To minimize uncontrolled variation, tissue was collected at both locations at approximately the same pre-flowering developmental stage, and standardized protocols for tissue collection, library preparation, sequencing, and data processing were applied across all samples.

      Although individual genotype-by-environment combinations were not biologically replicated, the analyses leverage substantial replication at the level of alleles and population structure, as individual alleles and transcripts are represented across many genetically distinct accessions spanning multiple ecotypes. This is analogous to many human population-scale transcriptomic studies, where each genotype is typically represented by a single biological sample and inference is made across genetically diverse individuals rather than replicated measurements of the same genotype.

      Importantly, the goal of this study was to identify transcriptomic patterns that are predictive of phenotypic variation across genotypes and environments, rather than to establish causal regulatory relationships. Accordingly, the genes and interaction patterns identified through model interpretation should be viewed as candidate predictive associations and hypothesis-generating signals that warrant validation in future studies with replicated sampling and controlled experiments. We have revised the manuscript to more clearly acknowledge these limitations and to align the interpretation of gene importance and interaction analyses with the predictive scope of the study. 

      Four, the analysis of expression differences across environments is based on a simple subtraction (TX - MI) followed by correlation with genetic similarity. This approach is not standard in transcriptomic analysis and does not account for variability, replication, or statistical uncertainty. Conventional methods for assessing differential expression and genotype-by-environment interactions rely on model-based frameworks that explicitly estimate variance components and test for interaction effects. Without such modeling, the observed expression differences may reflect noise or confounding factors rather than genotype-driven responses.

      We appreciate this comment. Our approach to estimating expression differences across environments was intended to quantify transcriptomic plasticity and evaluate whether variation in gene expression responses across environments could help explain and predict phenotypic plasticity. While this is not a conventional differential expression framework, our objective was not to identify differentially expressed genes or formally test genotype-by-environment interaction effects at the transcript level.

      Expression differences were calculated as TX − MI using log-transformed expression values. On the logarithmic scale, this difference is equivalent to the log expression ratio between environments and therefore provides a straightforward measure of the magnitude and direction of transcriptomic responses across locations. We used this metric as a descriptive measure of transcriptomic plasticity that could be related to genetic similarity and incorporated into predictive models of phenotypic plasticity. Consistent with this rationale, models based on expression differences across environments were able to predict differences in phenotypic performance between locations.

      (2) SHAP contribution values

      Although SHAP is a well-established framework for decomposing model predictions into feature-level contributions, its use in this manuscript raises several concerns regarding interpretation, statistical validity, and biological inference.

      First, SHAP values quantify the contribution of features within the fitted model, conditional on the joint distribution of inputs and the model structure. They do not represent causal effects or direct biological importance. 

      We agree that SHAP values do not represent causal effects or direct measures of biological importance. Rather, SHAP quantifies the contribution of features to model predictions within the context of a fitted model and the observed input distribution. Our rationale for using SHAP was not to infer causality, but to identify features that are consistently informative for predicting phenotypic variation. The underlying hypothesis is that features contributing strongly to accurate predictions may be associated with biological processes influencing the trait and can therefore serve as candidate genes or genomic regions for further investigation. This strategy has been widely adopted in genomics and plant biology, where interpretable machine learning methods are used to extract biological insights from predictive models. Accordingly, the genes and interactions highlighted by SHAP should be interpreted as predictive associations and candidate mechanisms requiring validation through independent experiments and functional studies. We have revised the manuscript to more clearly distinguish predictive importance from causal biological effects.

      There is a difference where SHAP values are often in log-odds and the regression model uses absolute units. 

      We thank the reviewer for this comment. We believe there may be a misunderstanding regarding the scale of the SHAP values reported in this study. The models analyzed here are regression models predicting continuous traits, not classification models. Consequently, SHAP values are expressed in the same units as the predicted phenotype and represent the additive contribution of each feature to individual predictions. The interpretation of SHAP values in log-odds units is specific to certain classification models where predictions are generated on the log-odds scale. We have revised the manuscript to clarify this point and avoid potential confusion.

      Without a fair evaluation of model fit, the interpretation of SHAP values needs to take a cautious step because a model could fit poorly when a feature shows very high SHAP values.

      We agree that SHAP values should be interpreted in the context of model performance. For this reason, model performance was evaluated prior to feature interpretation. Figure 1 presents the predictive performance of all models, and we focused our biological interpretation on flowering time and biomass because these traits are agronomically important and showed relatively stronger predictive performance compared to other traits, providing a more reliable basis for interpreting feature importance. We have revised the manuscript to emphasize that model interpretation should be considered in conjunction with model accuracy.

      In genomic data, where features are highly correlated due to linkage disequilibrium and co-expression, SHAP values can distribute contribution values across correlated variables in ways that are not uniquely identifiable. As a result, features highlighted as "important" may reflect correlation structure rather than true functional relevance.

      We agree that multicollinearity can complicate the interpretation of feature importance, as correlated features may share predictive information, resulting in importance being distributed among them or assigned preferentially to one feature over another. This issue is particularly relevant in genomic datasets due to linkage disequilibrium among SNPs and co-expression among genes. To reduce redundancy among genetic markers, SNPs were filtered using a linkage disequilibrium threshold of r<sup>2</sup> < 0.1 prior to model training, substantially reducing the number of highly correlated markers. We acknowledge, however, that correlation among features, particularly among co-expressed genes, remains present and may influence SHAP-based feature attribution. 

      To improve the robustness of feature interpretation, models were trained across 10 independent runs and SHAP values were averaged across runs, reducing sensitivity to individual model fits and providing more stable estimates of feature contributions. Nevertheless, SHAP values should not be interpreted as uniquely identifying the most functionally relevant feature within a correlated group of predictors. The genes and genetic variants highlighted by SHAP should therefore be viewed as candidate predictive features associated with trait variation rather than definitive evidence of causal biological relevance.

      This correlative structure can be exacerbated in this manuscript because of the use of TPM-normalized transcript abundances as predictor variables without biological replicates. Assume the estimates of transcript abundances are robust, TPM values are compositional, with a constant-sum constraint that creates dependencies among all genes that induce negative correlations. This issue is particularly relevant for the interpretation of gene importance and interaction effects, where correlated predictors can lead to unstable and non-unique attributions. This biological interpretation of transcript-based features remains uncertain.

      We agree that TPM-normalized expression values are compositional and may introduce dependencies among genes due to the constant-sum constraint. TPM normalization was selected because it facilitates comparisons of transcript abundance across samples by accounting for differences in sequencing depth and transcript length. Expression values were further transformed using log<sub>10</sub>(TPM + 1), which reduces skewness and stabilizes variance, making the data more suitable for machine learning analyses. We acknowledge that compositional effects and gene co-expression may influence feature attribution and the interpretation of gene importance. However, the primary objective of this study was predictive rather than inferential. In this context, log-transformed TPM values provide a standardized and comparable representation of transcript abundance across genotypes and environments, serving as consistent input for predictive models. Consequently, genes identified through SHAP analyses should be interpreted as candidate predictive features associated with trait variation, rather than as definitive evidence of direct causal biological importance.

      (3) Result interpretation

      For example, in page 11, "plasticity SNP- and transcriptomic-based models generally outperformed single-environment models for traits with low cross-environment correlation, such as green-up (Fig. 2c, r = -0.13, p < 8.3 × 10<sup>-3</sup>) and tiller count (Fig. 2f, r = -0.08, p = 0.1) (Supplementary Fig. S1)." is too broad. For green-up, the Diff model appears much better than MI, but not clearly better than TX.

      We thank the reviewer for this observation and agree that the original wording was too broad. We have revised the text to more accurately reflect the observed pattern, noting that difference-based models showed relatively stronger predictive performance for traits with low cross-environment correlation, while generally performing worse than single-environment models for traits with high cross-environment correlation. We believe this pattern is biologically meaningful, as transcriptomic differences across environments are expected to be most informative when phenotypic responses differ substantially between locations. We have revised to more accurately characterize these results.

      And, same page 11, "...Diffexp was more predictive than SNPs for trait plasticity in biomass, flowering time, and tiller count..." only holds true for biomass, not flowering time, or tiller count.

      We thank the reviewer for this observation and agree that the original statement was too strong. Diffexp models showed higher predictive performance for biomass and tiller count, whereas performance for flowering time was comparable between Diffexp and SNP models. We have revised to more accurately characterize these results.

      The aspect of "complementary information" between SNP and transcriptomic models in page 12 is stronger than what is supported by Figure 2. Figure 2 shows different predictive performance, but it does not by itself demonstrate complementarity. Establishing complementarity requires evidence that combining SNP+T improves prediction consistently or captures distinct, non-overlapping signals. Yet the preceding section says SNP+T outperformed either single data type in only 15% of cases, with modest gains. This is confusing. Also, there was not G+T in Figure 2; it is SNP+T.

      We agree that the term "complementary" was stronger than what the results in Figure 2 directly support. We have revised the text to describe these findings more cautiously, emphasizing that SNP and transcriptomic models may capture partially distinct aspects of biological variation. In the following section, we further address this point by showing that the two model types identify largely distinct sets of predictive features, suggesting that they reflect different sources of information relevant to trait prediction. We also thank the reviewer for noting the SNP+T vs. G+T inconsistency, which has been corrected throughout the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) I found that the title does not properly reflect the content of the paper. The term "artificial intelligence" is a bit misleading: I was expecting deep learning-based predictions. GxE and plasticity are clear strengths of the paper that are not present at all in the title. The same holds for multi-omic integration. Maybe you could consider revisiting the title.

      We thank the reviewer for this suggestion and have revised the title to: "Multiomic integration using interpretable machine learning reveals genetic mechanisms of trait variation and phenotypic plasticity in switchgrass." This revision highlights the integration of genomic and transcriptomic data, explicitly mentions phenotypic plasticity, and replaces "artificial intelligence" with "interpretable machine learning," which more accurately describes the modeling framework employed.

      (2) In general, the quality of the figures is very poor, and it did not allow me to perform a deep assessment of their content, which is sometimes hardly visible.

      We agree that figure quality in the submitted PDF was suboptimal. The reduction in image resolution occurred during conversion from the original Word document to PDF. We have uploaded a revised manuscript with higher-resolution figures to ensure all elements are clearly visible.

      (3) To estimate plasticity in phenotypes and transcripts, you use differences between locations. Other options have been proposed in the literature, like ratios, for example. I think the choice of the difference is probably a good one, but maybe you could justify it in the introduction.

      We chose to estimate phenotypic and transcriptomic plasticity using differences between locations because this metric preserves both the magnitude and direction of environmental responses while avoiding the instability associated with ratio-based measures, particularly when values are small. Furthermore, because transcript abundance values were modeled using log<sub>10</sub>(TPM + 1)-transformed data, differences between environments are conceptually equivalent to log expression ratios, as log(A) − log(B) = log(A/B). This justification has been added to the manuscript.

      (4) Figure 1. I wonder why you computed correlation matrices between genotypes for transcriptomic data instead of simply covariance matrices, which would be closer to the genomic relationship matrix.

      Our intention in Figure 1 was to visualize overall similarity among samples based on transcriptomic expression profiles across genes. We used Pearson correlation because it is widely applied in transcriptomic analyses to capture relative expression patterns while remaining independent of absolute expression magnitude and scale differences among samples. Covariance matrices, by contrast, are strongly influenced by feature variance and scale, which can complicate interpretation of overall transcriptomic similarity in high-dimensional expression datasets.

      (5) Figure 1. You calculate correlations between phenotypic distance and genomic distances for the different traits, but I think this is somehow related to the genomic heritability estimation. Could you please elaborate on this or provide the rationale under your analysis?

      We agree that the correlation between genomic relationships and phenotypic similarity is conceptually related to genomic heritability, as both analyses evaluate the extent to which genetic similarity is associated with phenotypic variation. However, their objectives differ. The correlation analysis between SNP, eCor, and phenotypic distance was intended as an exploratory assessment of whether genetically or transcriptomically similar genotypes tend to exhibit similar phenotypic responses across traits and environments. Genomic heritability estimation, in contrast, provides a formal variance component framework to quantify the proportion of phenotypic variance explained by genome-wide marker information. While related conceptually, these analyses address complementary but distinct aspects of the relationship between genomic, transcriptomic, and phenotypic variation.

      (6) L163-164: "Because genotypes with similar genetic information are expected to exhibit similar trait value": this is the definition of the heritability, so I would say that this is true to the extent of the heritability of the trait... Maybe you could consider modifying this sentence.

      Thank you for this suggestion. Our intention was not to invoke heritability directly, but rather to assess the associations among kinship, eCor, and phenotypic similarity within each environment and to compare the patterns captured by these different data types. We agree that the original wording could be interpreted as implying a deterministic relationship between genetic similarity and phenotypic resemblance. We have revised the text to focus on the association between genetic relatedness and similarity in expression and phenotypic profiles.

      (7) Figure 2. For the prediction with transcriptomic data, I understood that you matched data by site for the prediction, which is OK. But I wonder what would happen if you had used transcriptomic data from the other site and vice versa for the prediction, how much predictability you would have lost? I guess that this would somehow be related to the extent of GxE for each trait. Maybe it would be something interesting to look at?

      We thank the reviewer for this thoughtful suggestion. While cross-environment prediction is an interesting avenue for future research, our primary objective was not to maximize prediction across environments, as predictive ability would be expected to decrease substantially under strong G×E effects. Instead, we focused on single-environment models to: (1) identify transcriptomic features associated with trait prediction within each environment, and (2) evaluate which features were consistently important across environments. Furthermore, because G×E effects were observed, we developed TX − MI difference models to identify transcriptomic features associated with phenotypic plasticity. We therefore believe that the within-environment and plasticity-focused analyses were more closely aligned with the objectives of this study than cross-environment prediction would have been.

      (8) The calculation of local SHAP values for each genotype mentioned L331 and the clustering of genotypes based on such values, as presented in Figure 4, are not presented in the method section. Could you please provide more details?

      The estimation of SHAP values is described in the Methods section. Briefly, local SHAP values were calculated for each genotype, and genotypes were subsequently clustered based on these values using hierarchical clustering with Euclidean distance.

      (9) Gene-by-gene interactions paragraph: I am a bit confused here regarding which omic was used for looking at the interaction. Naively, I would have gone with the SNPs, but maybe you would rather use the transcripts instead? This would explain why in Figure 5 c-h, the x axes seemed to correspond to transcripts rather than SNPs. Could you please clarify and explain why you focused here on transcripts rather than SNPs?

      We thank the reviewer for this comment. To clarify, gene–gene interactions were assessed using transcriptomic models. This choice was motivated by three observations: (1) transcriptomic features were enriched among the most important predictors in the multi-omic models; (2) transcriptomic models identified a greater number of benchmark genes than SNP-based models; and (3) gene expression data provide a more biologically interpretable framework for assessing gene–gene interactions, as transcripts more directly reflect gene activity and regulatory processes than individual genetic variants. We have revised the manuscript to make this rationale explicit.

      (10) Materials and Methods:

      - Were there replicates of the genotypes within each site?

      This diversity panel is part of a multi-institutional project maintaining 10 common gardens across the United States. Given the scale of the experiment and the large number of genotypes evaluated, the design included a single plant per genotype at each site rather than within-site replication. We acknowledge this as a limitation and have discussed it explicitly in the revised manuscript.

      - Have the data (phenotypic/transcriptomic) been corrected for spatial effects?

      Because only a single replicate per genotype was evaluated at each site, genotype and local spatial effects could not be estimated independently. Applying a spatial correction under these conditions could inadvertently remove true biological variation. The randomized honeycomb design was intended to minimize systematic spatial confounding; however, we acknowledge that residual environmental heterogeneity may still contribute to phenotypic and transcriptomic variation.

      - L502-503: Could you please write the model formally instead of R code?

      The model has been written formally.

      - Genomic heritability estimates: I wonder how useful they really are? I suppose you could have used the model L502-503 to estimate genomic heritability in general across the 2 sites.

      We agree that the mixed model described in the revised manuscript could be used to estimate genomic heritability jointly across both sites. In this study, however, genomic heritability was estimated within individual environments to evaluate trait predictability within each location and to assess the heritable component of phenotypic plasticity. Genomic heritability also provides an important benchmark for interpreting prediction results, as the theoretical upper limit of prediction accuracy is approximately proportional to the square root of heritability under quantitative genetic theory.

      - L523: I am not sure what LD> 0.1 means. Could you please give more details, e.g., which measure of LD, which software, and which parameters?

      We have clarified this point in the Methods. LD pruning was performed using the +prune plugin in BCFtools, with pairwise LD measured as the squared Pearson correlation coefficient (r<sup>2</sup>). SNPs with r<sup>2</sup> > 0.1 within a 5 Mb sliding window were removed to retain a set of more independent markers.

      - L526: How many leaves were sampled per plant? Or maybe a particular leaf was chosen?

      For each plant, we collected the fully expanded leaves at the highest point on each tiller. The proximal ~4 cm (length of microcentrifuge tube) of that harvested leaf was then flash-frozen in liquid nitrogen for subsequent RNA extractions.

      - L592-597: I understand that training and test sets were stratified according to biomass (log-transformed) in TX. Could you please explain why this trait/location was chosen for the stratification?

      Training/test stratification: We chose log-transformed biomass in TX for stratification because biomass is the primary agronomic trait of interest in switchgrass and exhibits greater phenotypic variation in TX compared to MI, allowing stratification to better preserve the full range of values in both the training and testing sets. The same training/testing split was subsequently applied to all analyses to ensure consistent and fair comparisons across traits, environments, and data types.

      (11) Data availability: Maybe the read counts could also be provided on top of the raw RNA sequences. This would ensure, to some extent, reproducibility of the analyses, especially because the code for processing RNA-seq reads does not seem to be released in the GitHub repository.

      The RNA-seq pipeline used in this study is described in the revised manuscript. The pipeline code is maintained in an internal GitLab repository at JGI and is therefore only accessible within JGI systems. To facilitate reproducibility, we have deposited the TPM count matrix used in our analyses in the following Zenodo repository: https://zenodo.org/records/21781035

      (12) Code availability: The code to run Bayesian regression models does not seem to be released.

      The code for the Bayesian regression models has been added to the manuscript repository.

      Reviewer #2 (Recommendations for the authors):

      The manuscript will benefit the most from additional biological replicates for RNAseq, as well as the phenology alignment with data collection. Further recommendations for improving the clarity in experimental design and alignment between claims and evidence.

      We agree that additional biological replication for RNA-seq and transcriptomic sampling across multiple developmental stages would further strengthen the study. However, given the scale of the experiment, 426 genotypes evaluated across contrasting field environments, generating replicated transcriptomic datasets at multiple developmental stages was not logistically or financially feasible. Despite these constraints, the experimental design enabled characterization of transcriptomic and phenotypic variation across a large natural diversity panel and the identification of predictive patterns associated with trait variation across environments. We have revised the manuscript to more clearly describe the experimental design, sampling strategy, and study limitations, and to ensure that conclusions are appropriately aligned with the evidence presented.

      Minor corrections:

      (1) MAF threshold in Line 522, page 27. It should be > 0.05?

      Corrected to > 0.05. 

      (2) SNP+T versus G+T.

      Revised to SNP+T consistently throughout the manuscript. 

      (3) Include FDR-adjusted p-value thresholds.

      FDR-adjusted p-value thresholds: For the Pearson correlations reported in this study, each represents a single pre-specified comparison rather than a family of simultaneous tests, and raw p-values are therefore appropriate. For the benchmark gene enrichment and Wilcoxon rank-sum tests, where multiple comparisons were performed across models and traits, FDR correction has been applied using the Benjamini– Hochberg procedure and the reported p-values have been updated accordingly. 

      (4) Avoid ambiguous phrasing like "generally consistent" and "generally outperformed".

      Revised throughout the manuscript. 

      (5) The mention of artificial intelligence in the Abstract is largely conceptual and reads more like discussion material rather than the main finding of the manuscript, per eLife's Abstract requirements.

      The mention of artificial intelligence has been revised in the Abstract in accordance with eLife's guidelines.

    1. eLife Assessment

      This useful study shows that loss of Samp1, a protein at the boundary of the cell nucleus, disrupts muscle cell development and is accompanied by changes in gene activity and genome organization. Some of the evidence is solid, but data to support the overall claims is judged to be incomplete. Controlled experiments and mutants to establish whether the changes in organization contribute to impaired development or follow from it would elevate the work. This work will be of interest to those in the genome organization and developmental fields.

    2. Reviewer #1 (Public review):

      Summary:

      The authors ask whether Samp1 contributes to the reorganization of the genome and the regulation of gene expression during muscle-cell differentiation. Using C2C12 cells, they find that Samp1 depletion impairs differentiation and is accompanied by altered transcriptional responses and changes in peripheral chromatin and chromosome 8 distribution. These findings support Samp1's involvement in the differentiation program, although the causal relationship between the spatial and transcriptional phenotypes remains unresolved.

      Strengths:

      The study addresses a biologically significant question: how proteins at the nuclear envelope contribute to the coordinated changes in genome organization and gene expression that accompany cell differentiation. Its main contribution is to extend the established requirement for Samp1 in myogenesis to measurable spatial and transcriptional phenotypes, providing a more specific basis for investigating its function.

      The complementary imaging approaches examine chromatin organization at different scales. Fluorescence ratiometric imaging assesses the relative distribution of chromatin reporters across the nucleus, whereas chromosome 8 imaging provides a chromosome-specific measurement. Agreement between these approaches strengthens the observation that Samp1-depleted cells undergo an altered spatial response to differentiation conditions, while leaving the underlying mechanism open.

      The four-time-point transcriptional analysis adds temporal information that an endpoint comparison would miss. It allows readers to examine baseline differences and subsequent responses to differentiation conditions, and provides a potentially useful resource for identifying genes and pathways associated with the impaired differentiation program.

      The work also builds on earlier validation with an independent depletion construct and rescue of differentiation by RNAi-resistant Samp1. This prior evidence strengthens the biological foundation of the present study. Together, the results identify Samp1 as a useful target for further investigation of the relationship between nuclear organization and muscle development.

      Weaknesses:

      The study leaves open how the differentiation, spatial, and transcriptional changes are connected. By day three, control cells have formed myotubes, whereas Samp1-depleted cells largely remain myoblasts. This is a consequence of the depletion and does not invalidate the comparison. However, it means that altered chromosome positioning could contribute to the differentiation defect or arise because differentiation has been disrupted. The authors acknowledge that the mechanism remains unresolved; the findings should therefore be interpreted as evidence that these processes depend on Samp1, without establishing that changes in genome positioning drive the transcriptional response.

      The earlier rescue of differentiation strengthens the evidence for Samp1's role in myogenesis. The new spatial and transcriptional findings, however, have not been tested by rescue. This leaves some uncertainty about whether these particular effects are specific to Samp1 depletion in the stable cell line used here.

      The connection between impaired gene repression and association with the nuclear lamina remains indirect. The published Lamin B1 DamID data describe normal differentiation and do not measure what happens to lamina association after Samp1 depletion. They support the proposed connection but cannot establish it.

      The imaging measurements also need a cautious interpretation. The peripheral chromatin index describes relative fluorescence enrichment, rather than directly measuring heterochromatin abundance. The biological meaning of changes such as an index of approximately 1.1 versus 1.3 is therefore not immediately clear. Similarly, the chromosome 8 analysis measures the distribution of chromosome-paint signal within five optical sections rather than the entire chromosome territory. The results support spatial differences, but their magnitude may depend on nuclear geometry, the sampled sections, and image-processing choices whose influence is not fully assessed.

      Some conclusions about the transcriptional response go beyond what the statistical comparisons directly test. A significant change during differentiation in control cells and a nonsignificant change in depleted cells do not, by themselves, establish that the responses differ. The comparison between groups at day three provides evidence of expression differences, but does not determine whether the changes from their respective starting points differ.

    3. Reviewer #2 (Public review):

      Summary:

      Kašnik et al. set out test whether the previously reported requirement for the inner nuclear protein Samp1in myogenic differentiation and its also previously reported role in chromatin organization at the nuclear periphery are functionally linked. The authors confirm using a previously characterized shRNA system in cultured C2C12 cells that Samp1 loss leads to myogenic differentiation defects. Loss of Samp1 results in gene expression changes, including of genes associated with myogenesis and also interfered with the formation of peripheral heterochromatin and peripheralization of chromosome 8 which has previously been reported to be associated with myogenic differentiation. Finally, many of the downregulated genes by Samp1 loss were found in chromatin domains bound by lamin B.

      Strengths:

      The strength of this study is that is represents a rather straightforward analysis of the loss of Samp1 in myogenic differentiation in vitro. The RNAseq data is informative and is in line with the observed differentiation defects. In addition, the experiments are clearly designed and cleanly performed.

      Weaknesses:

      The major weakness of the study is that major conclusions are strongly overinterpreted and the degree of advance compared to prior studies is limited.

      The study does not - as claimed by the authors - functionally link Samp1 to myogenic differentiation via its role in genome organization. As outlined by the authors there is already considerable evidence for a role of Samp1 in myogenic differentiation. There is also prior data to show that Samp1 loss prevents peripheralization of chromosome 8. The data reported in this study add further data to a possible functional link between these two events, but similar to the currently available data, the evidence remain circumstantial and they fall short of the authors' claim - and stated goal - of directly demonstrating a functional role of Samp1 in myogenesis via genome organization. The presented data does not rule out that the two effects represent unrelated functions of Samp1. The authors have not shown as claimed that Samp1 exerts its effect on myogenesis via genome reorganization.

      The most relevant piece of data to point to such a link is the DamID data in Figure 3 showing that many downregulated Samp1-sensitive genes reside in lamin B associated chromatin. However, in this experiment, critical controls are missing to demonstrate that this effect represents an enrichment of Samp1-sensitive genes (see Recommendations).

      While the RNAseq data offers the opportunity to speculate on pathways involved in myogenesis, none of the proposed pathways are experimentally tested to validate their functional roles. They remain hypothetical but are presented as demonstrated facts.

      Finally, many of the findings are confirmatory in nature, such as the finding that Samp1 inhibits myogenic differentiation, that differentiation is accompanied by chromatin peripheralization or affects MAPK signaling.

    4. Reviewer #3 (Public review):

      The study by Kašnik et al investigates the role of Samp1, an inner nuclear membrane protein, in muscle cell differentiation. Samp1 was already known to be required for myogenic differentiation and to influence chromatin organization at the nuclear periphery, but whether these two functions were connected was unclear. The authors set out to address this by asking whether the differentiation defect caused by Samp1 depletion is accompanied by failures in genome reorganization at the nuclear periphery.

      Using C2C12 mouse myoblasts with stable Samp1 knockdown, they combine RNA sequencing, immunofluorescence, FRIC analysis, and chromosome 8 DNA-FISH to track both the transcriptional and spatial consequences of Samp1 loss during differentiation. The goal is to show that Samp1 is needed not just for muscle gene activation but also for the peripheral repositioning of chromatin that normally accompanies differentiation, and that these two processes are linked.

      The biggest weakness of this study is that it does not explain how Samp1 actually controls chromatin organization at the nuclear periphery. Is it acting through emerin, the LINC complex, or lamin A/C? This is never addressed. At minimum, the authors should test whether a Samp1 mutant that cannot bind emerin or SUN2 can still rescue the chromatin and differentiation defects.

      Related to this, only chromosome 8 is examined for repositioning defects. This chromosome was deliberately chosen because it is known to move during myogenesis, so it is not surprising that it fails to reposition upon Samp1 depletion. A chromosome that does not normally reposition should be included to show the effect is specific, and genes that are expected to remain away from the periphery should also be examined to test whether Samp1 depletion causes indiscriminate rather than selective chromatin reorganization defects.

      More broadly, the study is largely descriptive. The main finding is that knocking down Samp1 disrupts gene expression and chromatin repositioning, and that these two things correlate. But which comes first? Does the chromatin move and then genes get silenced, or the other way around? This question is central to the paper's argument and is never tested. Without some attempt to address this, the study reads more as an observation than a mechanistic advance.

    5. Author response:

      We thank the reviewers for their careful evaluation of our manuscript and their constructive feedback. We appreciate the points raised and will take them into consideration when revising the manuscript.

      We plan to clarify and refine certain aspects of the interpretation and discussion, particularly regarding the relationship between Samp1, chromatin organization, and myogenic differentiation. We will also aim to strengthen the study with additional experiments and analyses where feasible, addressing the reviewers’ suggestions to the extent possible within the scope of the study.

      We believe these revisions will help improve the clarity and strength of the manuscript.

    1. Regularmente monitoreado, celosamente protegido, duramente custodiado. Violentamente penetrado.

      A 2026 esta frase podría estar en cualquier artículo sobr centros de datos y lo que realizan desde sus entrañas digitales en nuestra vida cotidiana.

    1. Case#: Variant not associated with a proband. Variant listed in supplementary materials in Supplementary Table 4

      DiseaseAssertion: Mucopolysaccharidosis type I, MPS I

      FamilyInfo: N/A

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: A79V

      Variant1ClinVarID: 1458769

      Variant1CAID: CA356987

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    2. Case#: Variant not associated with a proband. Variant listed in supplementary materials in Supplementary Table 4

      DiseaseAssertion: Mucopolysaccharidosis type I, MPS I

      FamilyInfo: N/A

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: H33Q

      Variant1ClinVarID: 2134405

      Variant1CAID: CA355945479

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    3. Case#: Variant not associated with a proband. Variant listed in supplementary materials in Supplementary Table 4

      DiseaseAssertion: Mucopolysaccharidosis type I, MPS I

      FamilyInfo: N/A

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: R89W

      Variant1ClinVarID: 580286

      Variant1CAID: CA2801156

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    4. Case#: Variant not associated with a proband. Variant listed in supplementary materials in Supplementary Table 4

      DiseaseAssertion: Mucopolysaccharidosis type I, MPS I

      FamilyInfo: N/A

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: A75T

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    5. Case#: Variant not associated with a proband. Variant listed in supplementary materials in Supplementary Table 4

      DiseaseAssertion: Mucopolysaccharidosis type I, MPS I

      FamilyInfo: N/A

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: G51D

      Variant1ClinVarID: 193061

      Variant1CAID: CA274902

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    6. Case#: Variant not associated with a proband. Variant listed in supplementary materials in Supplementary Table 4

      DiseaseAssertion: Mucopolysaccharidosis type I, MPS I

      FamilyInfo: N/A

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: Y76C

      Variant1ClinVarID: 552686

      Variant1CAID: CA2801140

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    1. Inhibition of iduronic acid biosynthesis by ebselen reduces glycosaminoglycan accumulation in mucopolysaccharidosis type I fibroblasts

      PMID: 34192316

      Gene: IDUA

      Disease: MPS1

      Inheritance: AR

      InsufficientEvidence: Study used fibroblasts from patients with MPS-1. Protein changes associated with the variants in the fibroblasts are provided, but no other patient details are provided.

    1. 38/M

      Case#: Patient 38, male, Han origin, 5 years old (age at diagnosis)

      DiseaseAssertion: MPS1-H/S

      FamilyInfo: N/A

      CasePresentingHPOs: Macrocephaly, Joint stiffness, Mongolian macula, Hepatosplenomegaly, Hernia (HP:0000256, HP:0001387, HP:0011369, HP:0001433, HP:0100790)

      CaseHPOFreeText: N/A

      CaseNotHPOs: Global Developmental Delay, Airway obstruction (HP:0001263, HP:0006536)

      CaseNotHPOFreeText: Cardiac disease

      CaseEnzymeAssay: IDUA activity 1.5 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.236C>T (p.A79V)

      Variant1ClinVarID: 1458769

      Variant1CAID: CA356987

      Variant2: c.236C>T (p.A79V)

      Variant2ClinVarID: 1458769

      Variant2CAID: CA356987

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed in trans by parental testing.

      PreviouslyPublished N/A

    2. 22/M

      Case#: Patient 22, male, Han origin, 1 year 8 months old (age at diagnosis)

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Global Developmental Delay, Macrocephaly, Joint stiffness, Mongolian macula, Hepatosplenomegaly, Hernia, Airway obstruction (HP:0001263, HP:0000256, HP:0001387, HP:0011369, HP:0001433, HP:0100790, HP:0006536)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: Cardiac disease

      CaseEnzymeAssay: IDUA activity 1.8 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.236C>T (p.A79V)

      Variant1ClinVarID: 1458769

      Variant1CAID: CA356987

      Variant2: c.236C>T (p.A79V)

      Variant2ClinVarID: 1458769

      Variant2CAID: CA356987

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed by parental testing.

      PreviouslyPublished N/A

    3. 18/F

      Case#: Patient 18, female, Han origin, 1 year 6 months old (age at diagnosis)

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Global Developmental Delay, Macrocephaly, Joint stiffness, Mongolian macula, Airway obstruction (HP:0001263, HP:0000256, HP:0001387, HP:0011369, HP:0006536)

      CaseHPOFreeText: Cardiac disease

      CaseNotHPOs: Hepatosplenomegaly, Hernia (HP:0001433, HP:0100790)

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity 0.6 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.2T>C (p.Met1Thr)

      Variant1ClinVarID: 639529

      Variant1CAID: CA2801095

      Variant2: c.208C>T (p.Q70X)

      Variant2ClinVarID: 11909

      Variant2CAID: CA204563

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed in trans by parental testing.

      PreviouslyPublished N/A

    4. 4/F

      Case#: Patient 4, female, Han origin, 3 years old (age at diagnosis)

      DiseaseAssertion: MPS1-H/S

      FamilyInfo: N/A

      CasePresentingHPOs: Macrocephaly, Joint stiffness, Mongolian macula, Hepatosplenomegaly, Hernia (HP:0000256, HP:0001387, HP:0011369, HP:0001433, HP:0100790)

      CaseHPOFreeText: N/A

      CaseNotHPOs: Global Developmental Delay, Airway obstruction (HP:0001263, HP:0006536)

      CaseNotHPOFreeText: Cardiac disease

      CaseEnzymeAssay: IDUA activity 1.0 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.296+4G>A, it looks like this is a typo and should be c.299+4G>A

      Variant1ClinVarID: N/A

      Variant1CAID: CA2669476183

      Variant2: c.236C>T (p.A79V)

      Variant2ClinVarID: 1458769

      Variant2CAID: CA356987

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed in trans by parental testing.

      PreviouslyPublished N/A

    5. 37/M

      Case#: Patient 37, male, Han origin, age at diagnosis not available

      DiseaseAssertion: N/A

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity 1.5 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.2T>C (p.Met1Thr)

      Variant1ClinVarID: 639529

      Variant1CAID: CA2801095

      Variant2: c.2T>C (p.Met1Thr)

      Variant2ClinVarID: 639529

      Variant2CAID: CA2801095

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed by parental testing.

      PreviouslyPublished N/A

    6. 52/F

      Case#: Patient 52, female, Han origin, 2 years old (age at diagnosis)

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Global Developmental Delay, Macrocephaly, Joint stiffness, Mongolian macula, Hepatosplenomegaly, Hernia, Airway obstruction (HP:0001263, HP:0000256, HP:0001387, HP:0011369, HP:0001433, HP:0100790, HP:0006536)

      CaseHPOFreeText: Cardiac disease

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity 1.1 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1210G>T (p.E404X)

      Variant1ClinVarID: 552095

      Variant1CAID: N/A

      Variant2: c.1877G>A (p.W626X)

      Variant2ClinVarID: 928997

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed in trans by parental testing.

      PreviouslyPublished N/A

    7. 17/M

      Case#: Patient 17, male, Han origin, 15 years old (age at diagnosis)

      DiseaseAssertion: MPS1-S

      FamilyInfo: N/A

      CasePresentingHPOs: Joint stiffness (HP:0001387)

      CaseHPOFreeText: N/A

      CaseNotHPOs: Global Developmental Delay, Macrocephaly, Mongolian macula, Hepatosplenomegaly, Hernia, Airway obstruction (HP:0001263, HP:0000256, HP:0011369, HP:0001433, HP:0100790, HP:0006536)

      CaseNotHPOFreeText: Cardiac disease

      CaseEnzymeAssay: IDUA activity 1.1 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.236C>T (p.A79V)

      Variant1ClinVarID: 1458769

      Variant1CAID: CA356987

      Variant2: c.265C>T (p.R89W)

      Variant2ClinVarID: 580286

      Variant2CAID: CA2801156

      AdditionalVariants: N/A

      ParentalGenotype: The transmission of sequence variations found in each patient was inferred by sequencing the corresponding exons of the patient's parents - confirmed in trans by parental testing.

      PreviouslyPublished N/A

    8. 30/M

      Case#: Patient 30, male, Han origin, 37 years old (age at diagnosis)

      DiseaseAssertion: MPS1-S

      FamilyInfo: N/A

      CasePresentingHPOs: Joint stiffness, Airway obstruction (HP:0001387, HP:0006536)

      CaseHPOFreeText: Cardiac disease

      CaseNotHPOs: Global Developmental Delay, Macrocephaly, Mongolian macula, Hepatosplenomegaly, Hernia (HP:0001263, HP:0000256, HP:0011369, HP:0001433, HP:0100790)

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity 2.2 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.265C>T (p.R89W)

      Variant1ClinVarID: 580286

      Variant1CAID: CA2801156

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    9. 54/M

      Case#: Patient 54, male, Han origin, age of diagnosis not available

      DiseaseAssertion: N/A

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity 0.6 nmol/h/mg protein. The normal range in Chinese controls was found to be 27.2–52 nmol/h/mg protein.

      CaseUrineGAGs: Diagnosis was confirmed biochemically by the demonstration of an increased excretion of urinary dermatan sulfate and heparan sulfate.

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.236C>T (p.A79V)

      Variant1ClinVarID: 1458769

      Variant1CAID: CA356987

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. Case#: Patient 5, 9 years old, Iranian

      DiseaseAssertion: MPS1-H/S (Intermediate)

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity not detected

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    2. Case#: Patient 10, 10 years old, Iranian

      DiseaseAssertion: MPS1-S (Attenuated)

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 0.45 µmol/g.h (Normal ranges: 10-50 µmol/g.h)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.152G>A (p.G51D)

      Variant1ClinVarID: 193061

      Variant1CAID: CA274902

      Variant2: Unknown

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    3. Case#: Patient 8, 5 years old, Iranian

      DiseaseAssertion: MPS1-S (Attenuated)

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 0.04 µmol/g.h (Normal ranges: 10-50 µmol/g.h)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. 8/m

      Case#: Patient 8, female, 4.9 months (age at diagnosis), 8.6 months (age at HSCT), 6.7 - 9.5 months (age at ERT), 4 MRI + neuropsychological observations over the course of 6 years.

      DiseaseAssertion: MPS-1H

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 28.7 nmol/h/mg (Normal values: 11-43 nmol/h/mg)

      CaseUrineGAGs: N/A

      CaseERT: Yes

      CaseBMT: Yes, transplanted with an unrelated cord blood unit.

      Variant1: c.152G>A (p.G51D)

      Variant1ClinVarID: 193061

      Variant1CAID: CA274902

      Variant2: c.152G>A (p.G51D)

      Variant2ClinVarID: 193061

      Variant2CAID: CA274902

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. Case#: Patient #4 (see Table 2), male, 2 years old (age at diagnosis), 7 years old (age at report)

      DiseaseAssertion: MPS-I

      FamilyInfo: Born to a consanguineous marriage.

      CasePresentingHPOs: Failure to thrive, short stature, macroglossia, poor eyesight, hearing impairment, scoliosis, developmental delay (HP:0001508, HP:0004322, HP:0000158, HP:0000505, HP:0000365, HP:0002650, HP:0001263)

      CaseHPOFreeText: Extra abnormal body and abdominal inflammation

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: α-L-iduronidase 6.4 pmol/spot. The activity of this enzyme is below its reference (350–2600 pmol/spot)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    2. Case#: Patient #5 (see Table 2), female, 1 year old (age at diagnosis), 3 years old (age at report)

      DiseaseAssertion: MPS-I

      FamilyInfo: Born to a consanguineous marriage

      CasePresentingHPOs: Coarse facial features, macroglossia, developmental delay, failure to thrive, skeletal dysplasia (HP:0000256, HP:0000158, HP:0001263, HP:0001508, HP:0002652)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: α-L-iduronidase 1.5 pmol/spot. The activity of this enzyme is below its reference (200–2600 pmol/spot)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. Abnormal

      Case#: No additional case information provided

      DiseaseAssertion: MPSI-H

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: Abnormal

      CaseUrineGAGs: Abnormal

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.250G>A (p.G84S)

      Variant1ClinVarID: 726495

      Variant1CAID: CA2801152

      Variant2: c.250G>A (p.G84S)

      Variant2ClinVarID: 726495

      Variant2CAID: CA2801152

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. 103

      Case#: Pt 103 , Age 19 years, Female, DiseaseAssertion: MPS1 Scheie syndrome. FamilyInfo: NA CasePresentingHPOs: NA CaseHPOFreeText: NA CaseNotHPOs: NA CaseNotHPOFreeText: NA

      CaseEnzymeAssay: Low leukocyte IDUA enzyme activity CaseUrineGAGs: NA CaseERT: No CaseBMT: No Variant1: IVS7+1G>A This may be the variant c.972+1G>A, though cDNA not mentioned in text. Variant1ClinVarID: 198406 Variant1CAID: Variant2:p.X654R , cDNA not mentioned This variant may result from prev reported c.1960T>C ( ClinVar 960079) or from c.1960T>G ( ClinVar 11920) Variant2ClinVarID: Variant2CAID: AdditionalVariants: NA ParentalGenotype: NA PreviouslyPublished:NA

    2. 102

      Case#: Pt 102 , Age 37 years, Female, DiseaseAssertion: MPS1 Scheie syndrome. FamilyInfo: NA CasePresentingHPOs: NA CaseHPOFreeText: NA CaseNotHPOs: NA CaseNotHPOFreeText: NA

      CaseEnzymeAssay: Low leukocyte IDUA enzyme activity CaseUrineGAGs: NA CaseERT: No CaseBMT: No Variant1: p.Q70X Variant1ClinVarID: 11909 Variant1CAID: CA204563 Variant2:p.D445del Variant2ClinVarID: 840115 Variant2CAID: AdditionalVariants: NA ParentalGenotype: NA PreviouslyPublished:NA

    3. 205

      Case#: 205 5 yrs male, age at initial diagnosis 1.1 yrs DiseaseAssertion: MPS IH FamilyInfo:Nil CasePresentingHPOs: CaseHPOFreeText: CaseNotHPOs: CaseNotHPOFreeText: CaseEnzymeAssay: IDUA activity: low leukocyte IDUA enzyme activity CaseUrineGAGs: CaseERT: 1.5 yrs on ERT, then study drug, valanafusp alpha, also called AGT-181 for 12 months

      CaseBMT: No PreviouslyPublished Don't know Variant1:l Variant1: c.494-1G>A ClinVarID: 557942 Variant1CAID: Variant2:c.494-1G>A Variant2ClinVarID: 557942 Variant2CAID:

    1. Hurler disease

      Case#: Patient 4, 9 months old (age at report), male, Indian

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Coarse facial features, hepatosplenomegaly, joint contractures, hernia (HP:0000280, HP:0001433, HP:0002828, HP:0100790)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 1.6% (Range of residual enzyme activity: 0.0%-4.8%)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.223G>A (p.Ala75Thr)

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: c.223G>A (p.Ala75Thr)

      Variant2ClinVarID: 222993

      Variant2CAID: CA356994

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    2. Hurler disease

      Case#: Patient 3, 1 years old (age at report), female, Indian

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Coarse facial features, hepatosplenomegaly, joint contractures (HP:0000280, HP:0001433, HP:0002828)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 2.5% (Range of residual enzyme activity: 0.0%-4.8%)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.223G>A (p.Ala75Thr)

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: c.223G>A (p.Ala75Thr)

      Variant2ClinVarID: 222993

      Variant2CAID: CA356994

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    3. Hurler disease

      Case#: Patient 1, 1 years old (age at report), female, Indian

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Coarse facial features, hepatosplenomegaly, joint contractures, corneal clouding, recurrent infections, intellectual disability (HP:0000280, HP:0001433, HP:0002828, HP:0007957, HP:0002719, HP:0001249)

      CaseHPOFreeText: Cardiac disease

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 2.1% (Range of residual enzyme activity: 0.0%-4.8%)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.152G>A (p.Gly51Asp)

      Variant1ClinVarID: 193061

      Variant1CAID: CA274902

      Variant2: c.152G>A (p.Gly51Asp)

      Variant2ClinVarID: 193061

      Variant2CAID: CA274902

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    4. Hurler disease

      Case#: Patient 2, 5 months old (age at report), female, Indian

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: Coarse facial features, corneal clouding, recurrent infections, intellectual disability (HP:0000280, HP:0007957, HP:0002719, HP:0001249)

      CaseHPOFreeText: Cardiac disease

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 2.4% (Range of residual enzyme activity: 0.0%-4.8%)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.203G>A (p.Trp68Ter)

      Variant1ClinVarID: 2734634

      Variant1CAID: CA2801137

      Variant2: c.203G>A (p.Trp68Ter)

      Variant2ClinVarID: 2734634

      Variant2CAID: CA2801137

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. c.251G>C

      Case#: Functional assay variant with a clinical significance of being a VUS.

      DiseaseAssertion: MPS-I

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.251G>C (p.G84A)

      Variant1ClinVarID: 2203494

      Variant1CAID: CA355946494

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    2. c.299G>A

      Case#: Functional assay variant with a clinical significance of being a VUS.

      DiseaseAssertion: MPS-I

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.299G>A (p.R100K)

      Variant1ClinVarID: 281005

      Variant1CAID: CA2801160

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    3. c.223G>A

      Case#: Functional assay variant with a clinical significance of pathogenic.

      DiseaseAssertion: MPS-I

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.223G>A (p.A75T)

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    4. c.250G>A

      Case#: Functional assay variant with a clinical significance of being a VUS.

      DiseaseAssertion: MPS-I

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.250G>A (p.G84S)

      Variant1ClinVarID: 726495

      Variant1CAID: CA2801152

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. 5

      Case#: Patient 5, male, 1 year and 6 months (age at presentation)

      DiseaseAssertion: MPS1-H/S

      FamilyInfo: Consanguinity in the family present for this patient.

      CasePresentingHPOs: Corneal clouding, coarse facial features, dysostosis multiplex, hepatomegaly, joint contractures, extensive Mongolian spots (HP:0007957, HP:0000256, HP:0000943, HP:0002240, HP:0100790, HP:0011369)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.223G>A (p.A75T)

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: c.223G>A (p.A75T)

      Variant2ClinVarID: 222993

      Variant2CAID: CA356994

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    2. 3

      Case#: Patient 3, male, 1 year (age at presentation)

      DiseaseAssertion: MPS1-H

      FamilyInfo: Consanguinity in the family present for this patient.

      CasePresentingHPOs: Corneal clouding, coarse facial features, dysostosis multiplex, hydrocephalus, intellectual disability, extensive Mongolian spots (HP:0007957, HP:0000256, HP:0000943, HP:0000238, HP:0010864, HP:0011369)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.223G>A (p.A75T)

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: c.223G>a (p.A75T)

      Variant2ClinVarID: 222993

      Variant2CAID: CA356994

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    3. 4

      Case#: Patient 4, female, 2 years and 3 months (age at presentation)

      DiseaseAssertion: MPS1-H

      FamilyInfo: Consanguinity in the family present for this patient.

      CasePresentingHPOs: Coarse facial features, dysostosis multiplex, hepatomegaly, splenomegaly, intellectual disability (HP:0000256, HP:0000943, HP:0002240, HP:0001744, HP:0010864)

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.223G>A (p.A75T)

      Variant1ClinVarID: 222993

      Variant1CAID: CA356994

      Variant2: c.223G>A (p.A75T)

      Variant2ClinVarID: 222993

      Variant2CAID: CA356994

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. Patient offamily A

      Case#: Patient 1 of family A: Enrolled in study at 16 months, male, DiseaseAssertion:MPS1 FamilyInfo:Consanguinity Yes CasePresentingHPOs: Asthma HP:0002099, Sleep apnea HP:0010535, Multiple joint contractures HP:0002828, Kyphosis HP:0002808, Recurrent ear infections HP:0410018, Coarse facial features HP:0000280, Hepatosplenomegaly (HP:0001433), Inguinal hernia HP:0000023 Corneal opacity (HP:0007957), Joint stiffness (HP:0001387), CaseHPOFreeText: nose/throat infections, aggressive, heavy eye brows, large tongue, thick lips , rough skin CaseNotHPOs: CaseNotHPOFreeText: CaseEnzymeAssay: enzyme values lower than the cut-off values CaseUrineGAGs: NA CaseERT: NA CaseBMT:Nil Variant1:c.1456G>T ,Glu486 Variant1ClinVar ID: 651168 Variant1CAID: Variant2:c.1456G>T ,Glu486 Variant2ClinVarID:651168 Variant2CAID: AdditionalVariants:Nil ParentalGenotype:NA PreviouslyPublishedNA

    1. a female of Italian descent identified in 2018 (currently 2 years old) is compound heterozygous for two mutations with severe phenotypes (c.46_57del12/p.Y201X)

      Case#: P2 (IDUA-02)

      DiseaseAssertion: MPS1

      FamilyInfo: None provided

      CasePresentingHPOs:No info

      CaseHPOFreeText: No info

      CaseNotHPOs: No info

      CaseNotHPOFreeText: No info

      CaseEnzymeAssay: Newborn screening using dried blood spot by stable isotope dilution flow injection MS/MS %IDUA enzyme activity =0.17 microM/hour

      CaseUrineGAGs: Confirmatory testing using urinary GAGs (heparan [HS] and dermatan sulfate [DS] by LS-MS/MS method: DS=1.79 mg/L, HS=0.77 mg/L

      CaseERT: Started ERT (100 U/kg/Week) withing 15 days of birth

      CaseBMT: No info

      Variant1:c.46_57del12

      Variant1ClinVarID:92643

      Variant1CAID:

      Variant2: p.Y201X

      Variant2ClinVarID:1323100

      Variant2CAID:

      AdditionalVariants:

      ParentalGenotype: No info

      PreviouslyPublishedNot stated

    1. Q60X

      Case#: Family 10, UK

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: The paper states that "Hurler form is characterized by skeletal abnormalities, hepatosplenomegaly and severe mental retardation" but is not specific to this patient.

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: The α-L-iduronidase enzyme activities in leucocytes from all patients in the study was measured using the artificial substrate 4-methylumbelliferyl α-L iduronide (Sigma). Values ranged from 0 to 2.47 nmol/h/mg protein (reference range: 28–92 nmol/h/mg protein) and did not correlate to the clinical phenotype.

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.208C>T (p.Q70X)

      Variant1ClinVarID: 11909

      Variant1CAID: CA204563

      Variant2: c.178C>T (p.Q60X)

      Variant2ClinVarID: 655054

      Variant2CAID: CA355946098

      AdditionalVariants: N/A

      ParentalGenotype: Parental testing conducted, each parent was shown to carry one of the sequence changes found in their affected child.

      PreviouslyPublished N/A

    1. Case#: Patient 8, 5 years old, Iranian

      DiseaseAssertion: MPS1-S (Attenuated)

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 0.04 µmol/g.h (Normal ranges: 10-50 µmol/g.h)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    2. Case#: Patient 5, 9 years old, Iranian

      DiseaseAssertion: MPS1-H/S (Intermediate)

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: IDUA activity not detected

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    3. Case#: Patient 10, 10 years old, Iranian

      DiseaseAssertion: MPS1-S (Attenuated)

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: 0.45 µmol/g.h (Normal ranges: 10-50 µmol/g.h)

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.152G>A (p.G51D)

      Variant1ClinVarID: 193061

      Variant1CAID: CA274902

      Variant2: Unknown

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. Patient 2

      Case#: 2, Male, 4 years 4 months (diagnosed), 17 years (report), weight at presentation : NA, Height at presentation NA, Born in Uruguay

      Disease Assertion: Hurler Scheie ( attenuated phenotype)

      FamilyInfo: Parents are not consanguineous

      Case Presenting HPOs: HP: 0002808, HP: 0002937, HP: 0000280, HP:0000470, HP:0001376, HP:0034337, HP: 0000768, HP:0000943 (Kyphosis, Hemivertebrae, Coarse face, Short neck, Limitation of joint mobility, claw hands, pectus carinatum, dysostosis multiplex )

      CaseHPOFreeText: Bony dysplasia

      CaseNotHPOs: HP:0007957 HP:0001249, HP:0001433, ( Corneal opacity , Intellectual disability , visceromegaly)

      CaseNotHPOFreeText:

      CaseEnzymeAssay: IDUA enzyme in dried blood spot 0.26 nmol/h/L ( 2.20-11.70 )

      CaseUrineGAGs: NA CaseERT: N/A

      CaseBMT: N/A

      Genotyping Method: Sanger Sequencing

      Variant1: p.L18P (c.53C>T)

      Variant1ClinVarID: 193062

      Variant1CAID: NA

      Variant2: p.W402X

      Variant2ClinVarID: 11908

      Variant2CAID: 220498

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished: N/A

    1. c.1A>C

      Case#: No individual patient information available. 11/59 patients studied (18.7%) had this variant. This variant originates from two regions of Iran. 5 patients came from the city of Rasht in the Gilan province, making it a focal point for this variant. 6 patients came from five different provinces in the southwestern regions of Iran.

      DiseaseAssertion: MPS1

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: N/A

      Variant2ClinVarID: N/A

      Variant2CAID: N/A

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished This variant has previously been reported in studies conducted by Atceken et al. (2016) and Bertola et al. (2011), in patients from Turkey and Spain respectively. However, there has been no report suggestive of a founder effect for this variant.

    1. Table 2.

      Case#:Not assigned

      DiseaseAssertion:MPS type I with evidence of cognitive impairment defined as a score of at least 1 SD below mean on IQ testing or in one domain of neuropsychological function; attenuated MPS I

      FamilyInfo:No information

      CasePresentingHPOs:HP:0100543

      CaseHPOFreeText:

      CaseNotHPOs:HP:0032557

      CaseNotHPOFreeText:Subjects were excluded if they had undergone hematopoietic stem cell transplantation, were unable to comply with study procedures, had significant lumbar pathology precluding access to the intrathecal space via lumbar puncture, or significantly impaired spinal CSF flow as detected on a nuclear medicine flow study as part of screening.

      CaseEnzymeAssay:No information

      CaseUrineGAGs:No information

      CaseERT:Yes

      CaseBMT:No (exclusion criterion)

      Variant1:int3 -2a>g

      Variant1ClinVarID:

      Variant1CAID:

      Variant2:L238Q

      Variant2ClinVarID:265418

      Variant2CAID:

      AdditionalVariants:N/A

      ParentalGenotype:Not specified

      PreviouslyPublished

    2. Table 2.

      Case#:Not assigned

      DiseaseAssertion:MPS type I with evidence of cognitive impairment defined as a score of at least 1 SD below mean on IQ testing or in one domain of neuropsychological function; attenuated MPS I

      FamilyInfo:No information

      CasePresentingHPOs:HP:0100543

      CaseHPOFreeText:

      CaseNotHPOs:HP:0032557

      CaseNotHPOFreeText:Subjects were excluded if they had undergone hematopoietic stem cell transplantation, were unable to comply with study procedures, had significant lumbar pathology precluding access to the intrathecal space via lumbar puncture, or significantly impaired spinal CSF flow as detected on a nuclear medicine flow study as part of screening.

      CaseEnzymeAssay:No information

      CaseUrineGAGs:No information

      CaseERT:Yes

      CaseBMT:No (exclusion criterion)

      Variant1:L238Q

      Variant1ClinVarID:265418

      Variant1CAID:

      Variant2:W402X

      Variant2ClinVarID:

      Variant2CAID:

      AdditionalVariants:N/A

      ParentalGenotype:Not specified

      PreviouslyPublished

    3. Table 2.

      Case#:Not assigned

      DiseaseAssertion:MPS type I with evidence of cognitive impairment defined as a score of at least 1 SD below mean on IQ testing or in one domain of neuropsychological function; attenuated MPS I

      FamilyInfo:No information

      CasePresentingHPOs:HP:0100543

      CaseHPOFreeText:

      CaseNotHPOs:HP:0032557

      CaseNotHPOFreeText:Subjects were excluded if they had undergone hematopoietic stem cell transplantation, were unable to comply with study procedures, had significant lumbar pathology precluding access to the intrathecal space via lumbar puncture, or significantly impaired spinal CSF flow as detected on a nuclear medicine flow study as part of screening.

      CaseEnzymeAssay:No information

      CaseUrineGAGs:No information

      CaseERT:Yes

      CaseBMT:No (exclusion criterion)

      Variant1:L238Q

      Variant1ClinVarID:265418

      Variant1CAID:

      Variant2:63delC

      Variant2ClinVarID:

      Variant2CAID:

      AdditionalVariants:N/A

      ParentalGenotype:Not specified

      PreviouslyPublished

    4. Table 2.

      Case#:Not assigned

      DiseaseAssertion:MPS type I with evidence of cognitive impairment defined as a score of at least 1 SD below mean on IQ testing or in one domain of neuropsychological function; attenuated MPS I

      FamilyInfo:No information

      CasePresentingHPOs:HP:0100543

      CaseHPOFreeText:

      CaseNotHPOs:HP:0032557

      CaseNotHPOFreeText:Subjects were excluded if they had undergone hematopoietic stem cell transplantation, were unable to comply with study procedures, had significant lumbar pathology precluding access to the intrathecal space via lumbar puncture, or significantly impaired spinal CSF flow as detected on a nuclear medicine flow study as part of screening.

      CaseEnzymeAssay:No information

      CaseUrineGAGs:No information

      CaseERT:Yes

      CaseBMT:No (exclusion criterion)

      Variant1:65delC

      Variant1ClinVarID:

      Variant1CAID:

      Variant2:H240R

      Variant2ClinVarID:947028

      Variant2CAID:

      AdditionalVariants:N/A

      ParentalGenotype:Not specified

      PreviouslyPublished

    1. Case#: Pt 8, age is 8 months (proband can be found in Table 1)

      DiseaseAssertion: Hurler syndrome, mucopolysaccharidosis type IH, MPS IH, MPS type I

      FamilyInfo: No family information provided

      CasePresentingHPOs:

      CaseHPOFreeText:

      CaseNotHPOs:

      CaseNotHPOFreeText:

      CaseEnzymeAssay: In methods it is stated that an IDUA activity assay was conducted on cartilaginous pellets generated from patient-derived bone marrow stromal cells at 5 weeks. However, results of the IDUA activity assays are not stated.

      CaseUrineGAGs: "Significantly higher levels of GAGs in MPS IH compared with HD pellets at.1 week of chondrogenic differentiation, thus indicating that GAG accumulation in MPS IH can be seen during cell condensation and early differentiation stages." Total GAG content for each sample in ug GAG/ug DNA shown in Figure 4 graphs.

      CaseERT: Don't know

      CaseBMT: Don't know

      Variant1: p.G51D

      Variant1ClinVarID: 193061

      Variant1CAID: CA274902

      Variant2: p.G51D

      Variant2ClinVarID: 193061

      Variant2CAID: CA274902

      AdditionalVariants: No additional variants mentioned

      ParentalGenotype: No parental information provided

      PreviouslyPublished: No mention of being previously published

    1. patient with MPS

      Case#: No case information provided

      DiseaseAssertion: MPS-I

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: Exact number not stated but "significantly decreased".

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.2T>C (p.M1?)

      Variant1ClinVarID: 639529

      Variant1CAID: CA2801095

      Variant2: c.2T>C (p.M1?)

      Variant2ClinVarID: 639529

      Variant2CAID: CA2801095

      AdditionalVariants: N/A

      ParentalGenotype: Both parents were identified as carriers of the mutation

      PreviouslyPublished N/A

    1. Case#: Patient 1, Turkish

      DiseaseAssertion: MPS1-H

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CaseEnzymeAssay: N/A

      CaseUrineGAGs: N/A

      CaseERT: N/A

      CaseBMT: N/A

      Variant1: c.1A>C (p.M1L)

      Variant1ClinVarID: 550458

      Variant1CAID: CA355945204

      Variant2: c.1A>C (p.M1L)

      Variant2ClinVarID: 550458

      Variant2CAID: CA355945204

      AdditionalVariants: N/A

      ParentalGenotype: N/A

      PreviouslyPublished N/A

    1. 6

      Case: Patient 6 (Family C), male, 53yo

      DiseaseAssertion: VWD2B

      FamilyInfo: NA

      ParentalGenotype(s): NA

      CasePresentingHPOs: HP:0030129, HP:0030130, HP:0011871, HP:0012147

      (impaired ristocetin cofactor assay activity, Impaired von Willebrand factor collagen binding activity, Impaired RIPA, Reduced quantity of VWF)

      CaseHPOFreeText: blood group O, Platelet count x103/μl = 193, PFA 100 sec = 199, SPA % = 5.2, MADR mg/mL = 0.75, VWF:Ag U/dL = 47.1, VWF:CB U/dL = 44.6, VWF:CB ratio = 0.94, VWF:RCo U/dL = 38.0, VWF:RCo ratio = 0.80, Platelet VWF U/dL = 47.4, FVIII U/dL = 43.8, BAT F/M = 0

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: NA

      GenotypingMethod: Sequencing of exon 28 of VWF

      PreviouslyPublished: NA

      Gene: VWF

      Variant: c.4135C>T (p.Arg1379Cys)

      HGVS: NM_000552.4(VWF):c.4135C>T (p.Arg1379Cys)

      ClinVarID: 100333; https://www.ncbi.nlm.nih.gov/clinvar/variation/100333/

      CAID: NA

      gnomAD: MAF = 0.00003891 (5/128500 alleles) in the European (non-Finnish) population; https://gnomad.broadinstitute.org/variant/12-6128449-G-A?dataset=gnomad_r2_1

    2. 10

      Case: Patient 10 (Family D), female, 24yo

      DiseaseAssertion: VWD2B

      FamilyInfo: NA

      ParentalGenotype(s): NA

      CasePresentingHPOs: HP:0030129, HP:0030130, HP:0011871, HP:0012147

      (impaired ristocetin cofactor assay activity, Impaired von Willebrand factor collagen binding activity, Impaired RIPA, Reduced quantity of VWF)

      CaseHPOFreeText: blood group AB, Platelet count x103/μl = 231, PFA 100 sec = 223, SPA % = 6, MADR mg/mL = 0.75, VWF:Ag U/dL = 46.0, VWF:CB U/dL = 49.1, VWF:CB ratio = 1.07, VWF:RCo U/dL = 40.5, VWF:RCo ratio = 0.83, Platelet VWF U/dL = 55.6, FVIII U/dL = 64.8, BAT F/M = NA

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: NA

      GenotypingMethod: Sequencing of exon 28 of VWF

      PreviouslyPublished: NA

      Gene: VWF

      Variant: c.4135C>T (p.Arg1379Cys)

      HGVS: NM_000552.4(VWF):c.4135C>T (p.Arg1379Cys)

      ClinVarID: 100333; https://www.ncbi.nlm.nih.gov/clinvar/variation/100333/

      CAID: NA

      gnomAD: MAF = 0.00003891 (5/128500 alleles) in the European (non-Finnish) population; https://gnomad.broadinstitute.org/variant/12-6128449-G-A?dataset=gnomad_r2_1

    1. Case: P53, Chinese, female, 70yo

      DiseaseAssertion: VWD2A

      FamilyInfo: NA

      ParentalGenotype(s): NA

      CasePresentingHPOs:

      CaseHPOFreeText: HP:0012147

      (Reduced quantity of VWF)

      CaseNOTHPOs: type O blood, bleeding score = 9; VWF:Ag (%) = 32.1, VWF:RCo (%) = 6.0, FVIII:C (%) = 50.8, VWF:CB (%) = 14.7, VWFpp (%) = 56.9, VWF:RCo/VWF:Ag = 0.2, VWF:CB/VWF:Ag = 0.5, VWFpp/VWF:Ag = 1.8, FVIII:C/VWF:Ag = 1.6

      CaseNOTHPOFreeText:

      CasePreviousTesting: NA

      GenotypingMethod: NGS

      PreviouslyPublished: NA

      Gene: VWF

      Variant: c.4790G>T (p.Arg1597Leu)

      HGVS: NM_000552.4(VWF):c.4790G>T (p.Arg1597Leu)

      ClinVarID: 100392; https://www.ncbi.nlm.nih.gov/clinvar/variation/100392/

      CAID: NA

      gnomAD: absent

    2. Case: P44, Chinese, male, 6yo

      DiseaseAssertion: VWD2A

      FamilyInfo: NA

      ParentalGenotype(s): NA

      CasePresentingHPOs: HP:0012147

      (Reduced quantity of VWF)

      CaseHPOFreeText: type A blood, bleeding score = 6; VWF:Ag (%) = 43.6, VWF:RCo (%) = 18.3, FVIII:C (%) = 38.7, VWF:CB (%) = 16.7, VWFpp (%) = 64.8, VWF:RCo/VWF:Ag = 0.4, VWF:CB/VWF:Ag = 0.4, VWFpp/VWF:Ag = 1.5, FVIII:C/VWF:Ag = 0.9

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: NA

      GenotypingMethod: NGS

      PreviouslyPublished: NA

      Gene: VWF

      Variant 1: c.1870G>A (p.Gly624Ser)

      HGVS: NM_000552.4(VWF):c.1870G>A (p.Gly624Ser)

      ClinVarID: 619737; https://www.ncbi.nlm.nih.gov/clinvar/variation/619737/

      CAID: NA

      gnomAD: MAF = 0.004886 (59/12076 alleles; 1 homozygote) in the East Asian population; https://gnomad.broadinstitute.org/variant/12-6166098-C-T?dataset=gnomad_r2_1

      Variant 2: c.4883T>C (p.Ile1628Thr)

      HGVS: NM_000552.4(VWF):c.4883T>C (p.Ile1628Thr)

      ClinVarID: 284; https://www.ncbi.nlm.nih.gov/clinvar/variation/284/

      CAID: NA

      gnomAD: absent

    3. Case: P19, Chinese, male, 35yo

      DiseaseAssertion: VWD2M

      FamilyInfo: NA

      ParentalGenotype(s): NA

      CasePresentingHPOs: HP:0012147

      (Reduced quantity of VWF)

      CaseHPOFreeText: type O blood, bleeding score = 4; VWF:Ag (%) = 17.5, VWF:RCo (%) = 5.1, FVIII:C (%) = 35.9, VWF:CB (%) = 16.9, VWFpp (%) = 45.3, VWF:RCo/VWF:Ag = 0.3, VWF:CB/VWF:Ag = 1.0, VWFpp/VWF:Ag = 2.6, FVIII:C/VWF:Ag = 2.1

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: NA

      GenotypingMethod: NGS

      PreviouslyPublished: NA

      Gene: VWF

      Variant: c.4120C>T (p.Arg1374Cys)

      HGVS: NM_000552.4(VWF):c.4120C>T (p.Arg1374Cys)

      ClinVarID: 100329; https://www.ncbi.nlm.nih.gov/clinvar/variation/100329/

      CAID: NA

      gnomAD: MAF = 0.000008824 (1/113322 alleles) in the European (non-Finnish) population; https://gnomad.broadinstitute.org/variant/12-6128464-G-A?dataset=gnomad_r2_1

    1. Case UZ053: 28 year old female (row 54 in supplemental table)

      DiseaseAssertion: type 1 VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext:vWF:Ag (%) = 20.4; vWF:GPIbR (%) = 22.1; Act/Ag ratio = 1.08; FVIII:C (%) = 58.9; Platelets (x10^9/L) = 261

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: The Thrombosis-Hemostasis gene panel test used whole exome sequencing and included 99 diagnostic-grade genes (table 1 in PMID:36696193)

      GenotypingMethod: WES using the coagulation subpanel of the UZ Leuven Thrombosis-Haemostasis gene panel test. WES was performed on the sequencing platform NovaSeq 6000 (Illumina), and Alissa Interpret software v5.2-5.4 (Agilent) was used for variant interpretation. Assessment of copy number variation was validated within the bioinformatics pipeline. The American College of Medical Genetics and Genomics and the Association of Molecular Pathology guidelines were used to classify variants as pathogenic (P), likely pathogenic (LP), a variant of uncertain significance (VUS), or (likely) benign (LB, B) variants

      PreviouslyPublished:

      Gene: VWF

      Variant: c.449T>C p.Leu150Pro

      HGVS:

      ClinVarID: 100363

      CAID:

      gnomAD: 0.000001239 (2 in 1614236) no homozygotes https://gnomad.broadinstitute.org/variant/12-6110457-A-G?dataset=gnomad_r4

    2. 43 F Type 2 NOS VWD Type 2A/M VWD c.4120C>T p.Arg1374Cys P

      Case 14: 43 year old female, study ID UZ101 in supplemental table

      DiseaseAssertion: type 2 NOS VWD presequencing, type 2A/M VWD postsequencing

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: vWF:Ag (%) = 23.6; vWF:GPIbR (%) = 9; Act/Ag ratio = 0.38; FVIII:C (%) = 35.3; Platelets (x10^9/L) = 244

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: The Thrombosis-Hemostasis gene panel test used whole exome sequencing and included 99 diagnostic-grade genes (table 1 in PMID:36696193)

      GenotypingMethod: WES using the coagulation subpanel of the UZ Leuven Thrombosis-Haemostasis gene panel test. WES was performed on the sequencing platform NovaSeq 6000 (Illumina), and Alissa Interpret software v5.2-5.4 (Agilent) was used for variant interpretation. Assessment of copy number variation was validated within the bioinformatics pipeline. The American College of Medical Genetics and Genomics and the Association of Molecular Pathology guidelines were used to classify variants as pathogenic (P), likely pathogenic (LP), a variant of uncertain significance (VUS), or (likely) benign (LB, B) variants

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4120C>T p.Arg1374Cys

      HGVS:

      ClinVarID: 100329

      CAID:

      gnomAD: 0.000003098 (5 in 1613796) no homozygotes https://gnomad.broadinstitute.org/variant/12-6019298-G-A?dataset=gnomad_r4

    3. Case UZ090: 4 year old female (row 91 in supplemental table)

      DiseaseAssertion: type 1 VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: HP:0032370

      CaseHPOFreeRext: vWF:Ag (%) = 15; vWF:GPIbR (%) = 14.1; Act/Ag ratio = 0.94; FVIII:C (%) = 9.1; Platelets (x10^9/L) = 395

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: The Thrombosis-Hemostasis gene panel test used whole exome sequencing and included 99 diagnostic-grade genes (table 1 in PMID:36696193)

      GenotypingMethod: WES using the coagulation subpanel of the UZ Leuven Thrombosis-Haemostasis gene panel test. WES was performed on the sequencing platform NovaSeq 6000 (Illumina), and Alissa Interpret software v5.2-5.4 (Agilent) was used for variant interpretation. Assessment of copy number variation was validated within the bioinformatics pipeline. The American College of Medical Genetics and Genomics and the Association of Molecular Pathology guidelines were used to classify variants as pathogenic (P), likely pathogenic (LP), a variant of uncertain significance (VUS), or (likely) benign (LB, B) variants

      PreviouslyPublished:

      Gene: VWF

      Variant: c.2573G>T p.Cys858Phe

      HGVS:

      ClinVarID: 100229

      CAID:

      gnomAD: absent

      MultipleGeneVariants:

      Gene: VWF

      Variant: c.7437+5G>A

      ClinVarID: not found

      gnomAD: 0.000001859 (3 in 1613728) no homozygotes https://gnomad.broadinstitute.org/variant/12-5976106-C-T?dataset=gnomad_r4

    4. 6 F Type 2 NOS VWD Type 2M VWD c.4195C>T p.Arg1399Cys LP

      Case 8: 6 y.o. female, study ID UZ026 in supplemental table

      DiseaseAssertion: type 2 NOS VWD presequencing, type 2M VWD postsequencing

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: HP:0032442

      CaseHPOFreeRext: vWF:Ag (%) = 30.2; vWF:GPIbR (%) = 8.5; Act/Ag ratio = 0.28; FVIII:C (%) = 32.4; Platelets (x10^9/L) = 503

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: The Thrombosis-Hemostasis gene panel test used whole exome sequencing and included 99 diagnostic-grade genes (table 1 in PMID:36696193)

      GenotypingMethod:WES using the coagulation subpanel of the UZ Leuven Thrombosis-Haemostasis gene panel test. WES was performed on the sequencing platform NovaSeq 6000 (Illumina), and Alissa Interpret software v5.2-5.4 (Agilent) was used for variant interpretation. Assessment of copy number variation was validated within the bioinformatics pipeline. The American College of Medical Genetics and Genomics and the Association of Molecular Pathology guidelines were used to classify variants as pathogenic (P), likely pathogenic (LP), a variant of uncertain significance (VUS), or (likely) benign (LB, B) variants

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4195C>T p.Arg1399Cys

      HGVS:

      ClinVarID: 100337

      CAID:

      gnomAD: 0.00005143 (83/1613776) 0 homozygotes https://gnomad.broadinstitute.org/variant/12-6019223-G-A?dataset=gnomad_r4

    5. 37 F Type 2 NOS VWD Type 2A VWD c.3569G>A p.Cys1190Tyr LP

      Case 11: 37 year old female, study ID UZ022 in supplemental table

      DiseaseAssertion: type 2 NOS VWD presequencing, type 2A VWD postsequencing

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: vWF:Ag (%) = 54.1; vWF:GPIbR (%) = 10.5; Act/Ag ratio = 0.19; FVIII:C (%) = 56.6; Platelets (x10^9/L) = 291

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting: The Thrombosis-Hemostasis gene panel test used whole exome sequencing and included 99 diagnostic-grade genes (table 1 in PMID:36696193)

      GenotypingMethod: WES using the coagulation subpanel of the UZ Leuven Thrombosis-Haemostasis gene panel test. WES was performed on the sequencing platform NovaSeq 6000 (Illumina), and Alissa Interpret software v5.2-5.4 (Agilent) was used for variant interpretation. Assessment of copy number variation was validated within the bioinformatics pipeline. The American College of Medical Genetics and Genomics and the Association of Molecular Pathology guidelines were used to classify variants as pathogenic (P), likely pathogenic (LP), a variant of uncertain significance (VUS), or (likely) benign (LB, B) variants

      PreviouslyPublished:

      Gene: VWF

      Variant: c.3569G>A p.Cys1190Tyr

      HGVS:

      ClinVarID: 626960

      CAID:

      gnomAD: 6.195e-7 (1 in 1614212) no homozygotes https://gnomad.broadinstitute.org/variant/12-6022005-C-T?dataset=gnomad_r4

      MultipleGeneVariants: F7:c.977G>A p.Arg326Gln (VUS)

    1. 3 Type 2A c.1324C>TNc.6536C>Tp.Arg442Cysp.Ser2179PheC1237D2-VWD2D4-C8-4htzhtzMissenseMissensena na 17 15

      Case: 3 (Also in row 1 table 6)

      DiseaseAssertion: type 2A VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = n/a; VWF:Ac [IU/dL] = 17; FVIII:C [IU/dL] = 15

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.1324C>T p.Arg442Cys

      HGVS:

      ClinVarID: 806808

      CAID:

      gnomAD: 0.00003098 (50 in 1614024) no homoztygotes https://gnomad.broadinstitute.org/variant/12-6064354-G-A?dataset=gnomad_r4

      MultipleGeneVariants:

      Gene: VWF

      Variant: c.6536C>T p.Ser2179Phe

      ClinVarID: 100442

      gnomAD: 6.196e-7 (1 in 1614072) no homozygotes https://gnomad.broadinstitute.org/variant/12-5993924-G-A?dataset=gnomad_r4

    2. 28 Type2Ac.4078G>TN p.Val1360Phe 28 A1 htz Missense A 39 7 61

      Case: IP #28 (also in row 14 table 6)

      DiseaseAssertion: type 2A VWF

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: HP:0032370 - blood group A

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 39; VWF:Ac [IU/dL] = 7; FVIII:C [IU/dL] = 61

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4078G>T p.Val1360Phe

      HGVS:

      ClinVarID: 1809581

      CAID:

      gnomAD: absent

    3. 9 Type2Ac.2771G>A* p.Arg924Gln 21 D3-VWD3htz Missense na 17 10 51

      Case: IP #9

      DiseaseAssertion: type 2A VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 17; VWF:Ac [IU/dL] = 10; FVIII:C [IU/dL] = 51

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.2771G>A p.Arg924Gln

      HGVS:

      ClinVarID: 100240

      CAID:

      gnomAD: 0.01774 (28642 in 1614096) 297 homozygotes https://gnomad.broadinstitute.org/variant/12-6031493-C-T?dataset=gnomad_r4

    4. 34

      Case: IP #34

      DiseaseAssertion: type 2A (IIA) VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 55; VWF:Ac [IU/dL] = 35; FVIII:C [IU/dL] = 60

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4517C>T p.Ser1506Leu

      HGVS:

      ClinVarID: 100369

      CAID:

      gnomAD: absent

    5. 26, 27 Heterozygous2Nc.3673T > G p.Cys1225Gly 27 D3-E3 htz Missense O/A 50/42 29/44 40

      Case: IP #26 in type 2N VWD sub-cohort

      DiseaseAssertion: type 2N VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: HP:0032442 - blood group O

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 50; VWF:Ac [IU/dL] = 29; FVIII:C [IU/dL] = 40

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.3673T>G p.Cys1225Gly

      HGVS:

      ClinVarID: 100272

      CAID:

      gnomAD: absent

    6. 12–15 c.5191T > A p.Ser1731Thr 30 A3 htz Missense O/A/na/na23/47/94/na 21/46/89/50 52 CSA /86 CSA /126 CSA /nana/na/49/25

      Case: IP #15 in type 2M VWD sub-cohort

      DiseaseAssertion: type 2M VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = n/a; VWF:Ac [IU/dL] = 50; FVIII:C [IU/dL] = n/a; VWF:CB (IU/dL) = 25

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.5191T>A p.Ser1731Thr

      HGVS:

      ClinVarID: 100420

      CAID:

      gnomAD: 0.001158 (1870 in 1614210) 5 homozygotes https://gnomad.broadinstitute.org/variant/12-6016636-A-T?dataset=gnomad_r4

    7. 33 U c.4120C > A p.Arg1374Ser 28 A1 htz Missense O 11 4 10

      Case: IP #33 in type U VWD sub-cohort

      DiseaseAssertion: unclassified VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: HP:0032442 - blood group O

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 11; VWF:Ac [IU/dL] = 4; FVIII:C [IU/dL] = 10

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4120C>A p.Arg1374Ser

      HGVS:

      ClinVarID: 100328

      CAID:

      gnomAD: absent

    8. 55-57Type2A(IIA)c.4825G>A p.Gly1609Arg 28 A2 htz Missense na/B/O71/46/53 17/12/15 na

      Case: IP #55

      DiseaseAssertion: type 2A (IIA) VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 71; VWF:Ac [IU/dL] = 17; FVIII:C [IU/dL] = n/a

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4825G>A p.Gly1609Arg

      HGVS:

      ClinVarID: 100395

      CAID:

      gnomAD: 0.000003098 (5 in 1614130) no homozygotes https://gnomad.broadinstitute.org/variant/12-6018593-C-T?dataset=gnomad_r4

    9. 24–31 U (2A/2M/1C) c.4120C > T p.Arg1374Cys C 28 A1 htz Missense A/A/A/A/O/na/O/A22/ 22/23 /18/11/21/20/3210/8/6/12/4/9/11/1440 CSA /26 CSA /23 CSA /22 CSA /11 CSA /42

      Case: IP #29 in type U VWD sub-cohort

      DiseaseAssertion: unclassified (2A/2M/1C) VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 21; VWF:Ac [IU/dL] = 9; FVIII:C [IU/dL] = 42

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.4120C>T p.Arg1374Cys

      HGVS:

      ClinVarID: 100329

      CAID:

      gnomAD: absent

    10. 12 U (2M/2B/1) c.3835G > A p.Val1279Ile 28 A1 htz Missense na 7 na 8

      Case: IP #12 in type U VWD sub-cohort

      DiseaseAssertion: unclassified (2M/2B/1) VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: VWF:Ag [IU/dL] = 7; VWF:Ac [IU/dL] = n/a; FVIII:C [IU/dL] = 8

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Sequence variations in the VWF were identified through direct sequencing, while the detection of large rearrangements utilized the multiplex ligation-dependent probe amplification (MLPA) technique or assessed copy number variations (CNVs). Sanger sequencing methodology was applied for VWF analysis in patients investigated prior to 2016 (approximately 14% of IPs), whereas next-generation sequencing (NGS) was employed for the analysis of VWF in patients inspected from 2017 onwards (about 86% of IPs). In both strategies, the entire VWF, encompassing all 52 exons, intron/exon boundaries, and promoter regions, was analyzed.

      PreviouslyPublished:

      Gene: VWF

      Variant: c.3835G>A p.Val1279Ile

      HGVS:

      ClinVarID: 100288

      CAID:

      gnomAD: 0.0003751 (605 in 1612942) 1 homozygote https://gnomad.broadinstitute.org/variant/12-6019583-C-T?dataset=gnomad_r4

    1. Case: Case 2, male, newborn

      DiseaseAssertion: VWD2B

      FamilyInfo: First child of consanguineous parents (first cousins), no family history of bleeding, search of maternal platelet autoantibodies was negative; parents had VWF measurements showed decreased VWF:RCo, ratio FVIII:C/VWF:Ag < 0.5, and enhanced RIPA

      ParentalGenotype(s): both parents heterozygous for both variants

      CasePresentingHPOs: HP:0012330, HP:0001873, HP:0000967, HP:0030129

      (Pyelonephritis, thrombocytopenia, petechiae, impaired ristocetin cofactor assay activity)

      CaseHPOFreeText: born at 40wks, thrombocytopenia: 5 GL−1 at birth, platelets clumps in citrate tube, normal platelet morphology, no platelet incompatibility between the mother and the child in HPA groups 1, 3, 5, and 15, Allo- and autoantibodies on the mother and child and cross-match between father and mother serum were negative; bone marrow aspiration showed hyperplasia of megakaryocyte line, with all stages of maturation, consistent with peripheral origin, normal development; VWF:RCo = 13 IU dL−1, VWF:Ag = 47 IU dL−1, VWF:RCo /VWF:Ag= 0.3, FVIII:C = 22 IU dL−1, FVIII/VWF:Ag= 0.6, VWF:FVIII = 10 IU dL−1< RIPA analysis could not be performed

      CaseNOTHPOs: HP:0032169, HP:0001892

      (infection, abnormal bleeding)

      CaseNOTHPOFreeText: DIC

      CasePreviousTesting: NA

      GenotypingMethod: Sequencing of exon 20 and 28 of VWF gene

      PreviouslyPublished: NA

      Gene: VWF

      Variant 1: c.4010C>T (p.Pro1337Leu); exon 28, homozygous

      HGVS: NM_000552.4(VWF):c.4010C>T (p.Pro1337Leu)

      ClinVarID: 100314; https://www.ncbi.nlm.nih.gov/clinvar/variation/100314/

      CAID: NA

      gnomAD: MAF = 0.00005440 (1/18384 alleles) in East Asian population; https://gnomad.broadinstitute.org/variant/12-6128574-G-A?dataset=gnomad_r2_1

      Variant 2: c.2561G>A (p.Arg854Gln); exon 20; homozygous

      HGVS: NM_000552.4(VWF):c.2561G>A (p.Arg854Gln)

      ClinVarID: 296; https://www.ncbi.nlm.nih.gov/clinvar/variation/296/

      CAID: NA

      gnomAD: MAF = 0.005692 (143/25122 alleles, 4 homozygotes) in European (non-Finnish) population; https://gnomad.broadinstitute.org/variant/12-6143978-C-T?dataset=gnomad_r2_1

    1. T1-4

      Case: T1-4, male, 64yo

      DiseaseAssertion: VWD1

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeText: Blood type: unknown, bleeding score = 12, VWF:Ag (IU/mL) = 0.44; VWF:RCo (IU/mL) = 0.44; FVIII:C (IU/mL) = 0.75

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: qRT-PCR

      PreviouslyPublished:

      Gene: VWF

      Variant: c.6599-20A>T

      HGVS: NM_000552.5(VWF):c.6599-20A>T

      ClinVarID: 100444; https://www.ncbi.nlm.nih.gov/clinvar/variation/100444/?oq=c.6599-20A%3ET&m=NM_000552.5(VWF):c.6599-20A%3ET

      CAID:

      gnomAD: MAF = 0.0002795 (other population)

    1. The older brother (hereafter called Patient A)

      Case: Patient A, male, 14 years old at WIL-29 study entry; ethnicity not reported.

      DiseaseAssertion: VWD3. The patient was initially suspected to have VWD2A based on the family history but was reclassified as VWD3 after next-generation sequencing.

      FamilyInfo: Patient A and Patient B are brothers from a consanguineous family. Both parents were diagnosed with VWD. Figure 2 shows both parents as heterozygous and Patient A and Patient B as homozygous for the familial VWF variant. The pedigree also includes another brother affected with VWD whose genetic testing was underway and a sister whose VWD diagnosis and genetic testing were pending.

      ParentalGenotype: Both parents were heterozygous for the familial VWF variant, as shown in Figure 2.

      CasePresentingHPOs: HP:0000421, HP:0030129, HP:0012147 (Epistaxis, Impaired ristocetin cofactor assay activity, Reduced quantity of VWF)

      CaseHPOFreeText: Recurrent spontaneous epistaxis. Historical VWF:RCo 1% and VWF:Ag 10%. VWF:RCo 1 IU/dL at WIL-31 screening. At the month 0 pharmacokinetic visit, pre-injection FVIII 12 IU/dL and VWF:RCo <5 IU/dL; after 60 IU/kg wilate, virtually no rise in VWF activity (maximum VWF:RCo 9 IU/dL, in vivo recovery 0.07 kg/dL). VWF inhibitors were confirmed present at WIL-31 study entry, and the patient remained inhibitor-positive at the 12-month visit.

      CaseNotHPOs: NA

      CaseNotHPOFreeText: No anaphylactic reactions or severe treatment-related adverse events were reported.

      CasePreviousTesting: Diagnosed with VWD shortly after birth and initially suspected to have VWD2A based on family history. Historical VWF activity and antigen testing, and subsequent pharmacokinetic and VWF inhibitor testing, were performed. No earlier molecular genetic testing was reported.

      GenotypingMethod: Next-generation sequencing performed in Bonn, Germany. Additional technical details regarding the sequencing assay or panel were not provided.

      Gene: VWF

      Variant: c.4094T>C p.Leu1365Pro, homozygous

      HGVS: NM_000552.5:c.4094T>C (p.Leu1365Pro)

      ClinVar: 2438545

      CAID: NA

      gnomAD: Absent (not reported in gnomAD; 0/251040 control chromosomes)

    1. Case: Row 6 in Supplemental Table 1.

      DiseaseAssertion: type 3 VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: FVIII:C/VWF:Ag = 3.3; VWFpp/VWF:Ag = 0.3

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Genetic analysis was performed at the laboratory of hematology of the Radboud University Medical Center in Nijmegen. Ion semiconductor sequencing (Ion-Torrent) was used to analyze the VWF gene from peripheral blood obtained in EDTA tubes or from saliva samples. The 52 exons of VWF gene were analyzed including ±20 bp exon-intron boundaries. If no VWF gene variant was detected, multiplex ligation-dependent probe amplification (MLPA) analysis was performed to find large deletions or duplications. Thus, MLPA was performed in 183 patients (46.9%). All variants were confirmed with Sanger sequencing.

      PreviouslyPublished:

      Gene: VWF

      Variant: 970C>T R324*

      HGVS: c.970C>T (p.Arg324Ter)

      ClinVarID: 100512

      CAID:

      gnomAD: absent

    2. Case: Row 1 Supplemental Table 1.

      DiseaseAssertion: type 1 VWD

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs:

      CaseHPOFreeRext: FVIII:C/VWF:Ag ratio = 1.5; VWFpp/ VWF:Ag = 2.0

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Ion semiconductor sequencing (Ion-Torrent) was used to analyze the VWF gene from peripheral blood obtained in EDTA tubes or from saliva samples. The 52 exons of VWF gene were analyzed including ±20 bp exon-intron boundaries. If no VWF gene variant was detected, multiplex ligation-dependent probe amplification (MLPA) analysis was performed to find large deletions or duplications. Thus, MLPA was performed in 183 patients (46.9%). All variants were confirmed with Sanger sequencing.

      PreviouslyPublished:

      Gene: VWF

      Variant: 55G>A G19R

      HGVS: c.55G>A p.G19R

      ClinVarID: 100432

      CAID:

      gnomAD: 0.00007062 (114 in 1614206) no homozygotes https://gnomad.broadinstitute.org/variant/12-6123142-C-T?dataset=gnomad_r4

    3. Supplementary Material

      Case: Row 3 in Supplemental Table 1. Represents 2 patients in 2 families.

      DiseaseAssertion: type 1 VWD

      FamilyInfo:

      ParentalGenotype(s): Both patients also had R2313C (novel)

      CasePresentingHPOs:

      CaseHPOFreeRext: FVIII:C/VWF:Ag = 2.5 (2.4-2.5); VWFpp/VWF:Ag = 1.8 (1.3-2.3)

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: Genetic analysis was performed at the laboratory of hematology of the Radboud University Medical Center in Nijmegen. Ion semiconductor sequencing (Ion-Torrent) was used to analyze the VWF gene from peripheral blood obtained in EDTA tubes or from saliva samples. The 52 exons of VWF gene were analyzed including ±20 bp exon-intron boundaries. If no VWF gene variant was detected, multiplex ligation-dependent probe amplification (MLPA) analysis was performed to find large deletions or duplications. Thus, MLPA was performed in 183 patients (46.9%). All variants were confirmed with Sanger sequencing.

      PreviouslyPublished:

      Gene: VWF

      Variant: 421G>A D141N

      HGVS: c.421G>A p.Asp141Asn

      ClinVarID: 100338

      CAID:

      gnomAD: absent

    1. 0.55

      Case: 2B-4

      DiseaseAssertion: VWD2B

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: AcuStar RCo/Ag = 0.55; AcuStar CB/Ag = 0.44; ELISA CB/Ag = 0.44

      CaseHPOFreeText:

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: not described

      PreviouslyPublished: 1373334

      Gene: VWF

      Variant: c.4010C>T (p.Pro1337Leu)

      HGVS: NM_000552.5(VWF):c.4010C>T (p.Pro1337Leu)

      ClinVarID: 100314; https://www.ncbi.nlm.nih.gov/clinvar/variation/100314/?new_evidence=false

      CAID:

      gnomAD: MAF = 0.00005440 in East Asian population; https://gnomad.broadinstitute.org/variant/12-6128574-G-A

    2. 0.74

      Case: 2B-10

      DiseaseAssertion: VWD2B

      FamilyInfo:

      ParentalGenotype(s):

      CasePresentingHPOs: AcuStar RCo/Ag = 0.74; AcuStar CB/Ag = 0.97; ELISA CB/Ag = 1.13

      CaseHPOFreeText:

      CaseNOTHPOs:

      CaseNOTHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: not described

      PreviouslyPublished: 11325649

      Gene: VWF

      Variant: c.4135C>T (p.Arg1379Cys)

      HGVS: NM_000552.5(VWF):c.4135C>T (p.Arg1379Cys)

      ClinVarID: 100333; https://www.ncbi.nlm.nih.gov/clinvar/variation/100333/?new_evidence=false

      CAID:

      gnomAD: MAF (European, non-Finnish) = 0.00003891; 0 homozygotes; https://gnomad.broadinstitute.org/variant/12-6128449-G-A

    1. P6

      Case: P6, female, 31yo

      DiseaseAssertion: VWD2M

      FamilyInfo: NA

      ParentalGenotype(s): NA

      CasePresentingHPOs: HP:0012147, HP:0030129, HP:0030130

      (Reduced quantity of VWF, Impaired ristocetin cofactor assay activity, Impaired von Willebrand factor collagen binding activity)

      CaseHPOFreeText: bleeding score = 3; VWF:Ag (%) = 23, VWF:RCo(%) = 4, VWF:RCo/VWF:Ag ratio = 0.17, VWF:CIIIB (%) = 9.4, VWF:CIIIB/VWF:Ag ratio = 0.4, VWF:CVIB (%) = 4, VWF:CVIB/VWF:Ag ratio = 0.17

      CaseNOTHPOs: HP:0030132

      (Absence of large von Willebrand factor multimers)

      CaseNOTHPOFreeText: NA

      CasePreviousTesting: NA

      GenotypingMethod: NGS

      PreviouslyPublished: NA

      Gene: VWF

      Variant: c.4121G>A (p.Arg1374His)

      HGVS: NM_000552.4(VWF):c.4121G>A (p.Arg1374His)

      ClinVarID: 100330; https://www.ncbi.nlm.nih.gov/clinvar/variation/100330/

      CAID:

      gnomAD: absent